4464 lines
629 KiB
Plaintext
4464 lines
629 KiB
Plaintext
{
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"cells": [
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"id": "c9af9f23",
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"status": "completed"
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},
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"tags": []
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},
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"source": [
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"# Analisis Sentimen dan Pemodelan Topik Ulasan Wisatawan tentang Lawang Sewu"
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]
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},
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{
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"cell_type": "markdown",
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"id": "be918bec",
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"metadata": {
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"duration": 0.007414,
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"end_time": "2025-08-17T11:14:38.081285",
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"status": "completed"
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"tags": []
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},
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"source": [
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"### 1. Judul & Tujuan Penelitian\n",
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"\n",
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"**Judul:** Analisis Sentimen dan Pemodelan Topik Ulasan Wisatawan tentang Lawang Sewu\n",
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"\n",
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"**Tujuan:**\n",
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"1. **Klasifikasi Sentimen:** Mengklasifikasikan ulasan dari Google Reviews ke dalam sentimen **positif, negatif, atau netral** secara terawasi (*supervised*).\n",
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"2. **Pemodelan Topik:** Mengungkap topik-topik utama yang dibicarakan dalam ulasan menggunakan *Latent Dirichlet Allocation* (LDA).\n",
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"3. **Analisis Terpadu:** Mengaitkan hasil sentimen dengan topik yang ditemukan untuk memberikan rekomendasi perbaikan layanan yang actionable bagi pengelola Lawang Sewu."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b567eac8",
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"metadata": {
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"papermill": {
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"duration": 0.007399,
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"end_time": "2025-08-17T11:14:38.096364",
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"exception": false,
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"start_time": "2025-08-17T11:14:38.088965",
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"status": "completed"
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},
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"tags": []
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},
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"source": [
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"### 2. Deskripsi Dataset & Sumber Data\n",
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"\n",
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"* **Sumber Data:** Google Maps/Reviews. Platform ini dipilih karena memiliki volume ulasan yang signifikan dibandingkan platform lain yang datanya \"terlalu sedikit dan tidak ada value\".\n",
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"* **Ukuran Dataset:** **3.500 ulasan** yang telah dilabeli secara manual dengan sentimen (positif, negatif, netral).\n",
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"* **Struktur Data (Kolom Kunci):**\n",
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" * `text`: Isi ulasan asli dari pengguna.\n",
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" * `label`: Sentimen yang telah ditentukan (POSITIF, NEGATIF, NETRAL).\n",
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" * *(Opsional)*: `date`, `rating`, `user_id` (jika tersedia dan dianonimkan).\n",
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"* **Catatan Etika & Legal:**\n",
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" * Proses *scraping* data dilakukan secara etis dan menghormati *Terms of Service* (TOS) Google.\n",
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" * Data pengguna seperti nama atau ID akan dianonimkan untuk menjaga privasi."
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]
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},
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{
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"cell_type": "markdown",
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"id": "2f8ed21a",
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"metadata": {
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"papermill": {
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"duration": 0.007957,
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"end_time": "2025-08-17T11:14:38.113743",
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"exception": false,
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"start_time": "2025-08-17T11:14:38.105786",
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"status": "completed"
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},
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||
"tags": []
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||
},
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||
"source": [
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"### 3. Ringkasan Alur Metodologi\n",
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"\n",
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"Penelitian ini akan mengikuti alur kerja standar dalam proyek *Natural Language Processing* (NLP):\n",
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"\n",
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"1. **Setup & Audit Data**: Mempersiapkan lingkungan kerja, memuat data, dan melakukan analisis data eksplorasi awal untuk memahami karakteristik dataset.\n",
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"2. **Preprocessing Teks**: Membersihkan dan menormalisasi data teks (termasuk *cleaning*, *case folding*, *tokenisasi*, *stopword removal*, dan *stemming*).\n",
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"3. **Deduplikasi**: Menghapus ulasan duplikat (baik yang identik maupun yang sangat mirip) untuk memastikan kualitas model.\n",
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"4. **Pembagian Data**: Membagi dataset secara *stratified* menjadi data latih, validasi, dan uji untuk evaluasi model yang adil.\n",
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"5. **Analisis Sentimen (Supervised):**\n",
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" * **Ekstraksi Fitur**: Mengubah teks menjadi representasi numerik menggunakan TF-IDF.\n",
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" * **Seleksi Model**: Melakukan *screening* cepat dengan LazyPredict, dilanjutkan dengan validasi silang pada model kandidat (LinearSVC, LogisticRegression, ComplementNB).\n",
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" * **Tuning & Evaluasi**: Melakukan *hyperparameter tuning* pada model terbaik dan mengevaluasi performanya secara terstruktur pada data uji.\n",
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"6. **Pemodelan Topik (Unsupervised):**\n",
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" * **Persiapan Korpus**: Membangun kamus dan korpus dari teks bersih.\n",
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" * **Pencarian Jumlah Topik**: Menggunakan metrik *Coherence Score* (c_v) untuk menemukan jumlah topik (k) yang optimal.\n",
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" * **Interpretasi & Visualisasi**: Melatih model LDA final dan membuat visualisasi interaktif dengan pyLDAvis.\n",
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"7. **Integrasi & Laporan**: Menggabungkan hasil sentimen dan topik untuk menarik kesimpulan dan memberikan rekomendasi."
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]
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},
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{
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"cell_type": "markdown",
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"id": "be9a727d",
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"status": "completed"
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},
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"tags": []
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},
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"source": [
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"### 4. Setup Lingkungan & Reproducibility\n",
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"\n",
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"Langkah pertama adalah mengimpor pustaka yang dibutuhkan, mengatur *seed* untuk reproduktifitas, dan membuat folder untuk menyimpan artefak hasil analisis."
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]
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||
},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "3103e169",
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"metadata": {
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"execution": {
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"papermill": {
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"status": "completed"
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},
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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||
"Collecting Sastrawi\r\n",
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||
" Downloading Sastrawi-1.0.1-py2.py3-none-any.whl.metadata (909 bytes)\r\n",
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"Collecting thefuzz\r\n",
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||
" Downloading thefuzz-0.22.1-py3-none-any.whl.metadata (3.9 kB)\r\n",
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"Collecting rapidfuzz<4.0.0,>=3.0.0 (from thefuzz)\r\n",
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" Downloading rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\r\n",
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"Downloading Sastrawi-1.0.1-py2.py3-none-any.whl (209 kB)\r\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.7/209.7 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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"\u001b[?25hDownloading thefuzz-0.22.1-py3-none-any.whl (8.2 kB)\r\n",
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"Downloading rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\r\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m40.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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||
"\u001b[?25hInstalling collected packages: Sastrawi, rapidfuzz, thefuzz\r\n",
|
||
"Successfully installed Sastrawi-1.0.1 rapidfuzz-3.13.0 thefuzz-0.22.1\r\n",
|
||
"Python version: 3.11.13 (main, Jun 4 2025, 08:57:29) [GCC 11.4.0]\n",
|
||
"pandas version: 2.2.3\n",
|
||
"scikit-learn version: 1.2.2\n",
|
||
"gensim version: 4.3.3\n",
|
||
"pyLDAvis version: 3.4.0\n",
|
||
"Setup selesai. Folder 'artefak/' siap digunakan.\n"
|
||
]
|
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}
|
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],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"import re\n",
|
||
"import time\n",
|
||
"import json\n",
|
||
"import os\n",
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||
"import sys\n",
|
||
"import joblib\n",
|
||
"from tqdm.notebook import tqdm\n",
|
||
"tqdm.pandas()\n",
|
||
"\n",
|
||
"# Library untuk NLP dan Machine Learning\n",
|
||
"import sklearn\n",
|
||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||
"from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold, GridSearchCV\n",
|
||
"from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
|
||
"from sklearn.svm import LinearSVC\n",
|
||
"from sklearn.linear_model import LogisticRegression\n",
|
||
"from sklearn.naive_bayes import ComplementNB\n",
|
||
"from sklearn.pipeline import Pipeline\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.neighbors import NearestCentroid\n",
|
||
"\n",
|
||
"from lightgbm import LGBMClassifier\n",
|
||
"\n",
|
||
"import gensim\n",
|
||
"from gensim.corpora import Dictionary\n",
|
||
"from gensim.models import LdaMulticore, CoherenceModel\n",
|
||
"import pyLDAvis.gensim_models as gensimvis\n",
|
||
"import pyLDAvis\n",
|
||
"\n",
|
||
"!pip install Sastrawi thefuzz\n",
|
||
"from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
|
||
"from Sastrawi.StopWordRemover.StopWordRemoverFactory import StopWordRemoverFactory\n",
|
||
"\n",
|
||
"from thefuzz import fuzz # untuk near-duplicate detection\n",
|
||
"\n",
|
||
"# Pengaturan umum\n",
|
||
"sns.set_style('whitegrid')\n",
|
||
"SEED = 42\n",
|
||
"np.random.seed(SEED)\n",
|
||
"\n",
|
||
"# Buat folder artefak jika belum ada\n",
|
||
"if not os.path.exists('artefak'):\n",
|
||
" os.makedirs('artefak')\n",
|
||
" \n",
|
||
"print(\"Python version:\", sys.version)\n",
|
||
"print(\"pandas version:\", pd.__version__)\n",
|
||
"print(\"scikit-learn version:\", sklearn.__version__)\n",
|
||
"print(\"gensim version:\", gensim.__version__)\n",
|
||
"print(\"pyLDAvis version:\", pyLDAvis.__version__)\n",
|
||
"print(\"Setup selesai. Folder 'artefak/' siap digunakan.\")"
|
||
]
|
||
},
|
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{
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"cell_type": "markdown",
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"id": "6dfa6d81",
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"metadata": {
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"papermill": {
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"duration": 0.008034,
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"end_time": "2025-08-17T11:15:25.016066",
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"start_time": "2025-08-17T11:15:25.008032",
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"status": "completed"
|
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},
|
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"tags": []
|
||
},
|
||
"source": [
|
||
"### 5. Audit Data Awal\n",
|
||
"\n",
|
||
"Kita akan memuat dataset dan melakukan pemeriksaan awal untuk memahami struktur, tipe data, dan distribusi label sentimen."
|
||
]
|
||
},
|
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "09911f74",
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"tags": []
|
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},
|
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"outputs": [
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
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"text": [
|
||
"Dataset berhasil dimuat: 3506 baris dan 3 kolom.\n",
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||
"\n",
|
||
"Contoh 5 baris data awal:\n"
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]
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"<div>\n",
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"<style scoped>\n",
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" <th>No</th>\n",
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|
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" <td>1</td>\n",
|
||
" <td>Tempat wisata yang bersejarah</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>belum ke Semarang kalau belum datang ke lawang...</td>\n",
|
||
" <td>POSITIF</td>\n",
|
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" </tr>\n",
|
||
" <tr>\n",
|
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" <th>2</th>\n",
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||
" <td>3</td>\n",
|
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" <td>Ternyata ini dulunya gedung percetakan tiket k...</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>KAI dalam mengelola aset lebih profesional lag...</td>\n",
|
||
" <td>NEGATIF</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>5</td>\n",
|
||
" <td>Ticket masuk 25.000 per orang.. Air mineral 60...</td>\n",
|
||
" <td>NETRAL</td>\n",
|
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"0 1 Tempat wisata yang bersejarah POSITIF\n",
|
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|
||
"2 3 Ternyata ini dulunya gedung percetakan tiket k... NETRAL\n",
|
||
"3 4 KAI dalam mengelola aset lebih profesional lag... NEGATIF\n",
|
||
"4 5 Ticket masuk 25.000 per orang.. Air mineral 60... NETRAL"
|
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|
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|
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"<class 'pandas.core.frame.DataFrame'>\n",
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{
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"name": "stderr",
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"text": [
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"/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1498: DeprecationWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, pd.CategoricalDtype) instead\n",
|
||
" if pd.api.types.is_categorical_dtype(vector):\n",
|
||
"/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1498: DeprecationWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, pd.CategoricalDtype) instead\n",
|
||
" if pd.api.types.is_categorical_dtype(vector):\n"
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{
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"data": {
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\n",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Statistik Distribusi Label:\n",
|
||
"label\n",
|
||
"POSITIF 71.420422\n",
|
||
"NETRAL 15.516258\n",
|
||
"NEGATIF 11.523103\n",
|
||
"TIDAK RELEVAN 1.540217\n",
|
||
"Name: proportion, dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"NAMA_FILE_RAW = '/kaggle/input/labeledlawangsewureviewdata/LabeledLawangSewuReviewData.xlsx'\n",
|
||
"\n",
|
||
"try:\n",
|
||
" df_raw = pd.read_excel(NAMA_FILE_RAW)\n",
|
||
" print(f\"Dataset berhasil dimuat: {df_raw.shape[0]} baris dan {df_raw.shape[1]} kolom.\")\n",
|
||
"except FileNotFoundError:\n",
|
||
" print(f\"ERROR: File '{NAMA_FILE_RAW}' tidak ditemukan. Pastikan file berada di direktori yang sama.\")\n",
|
||
"\n",
|
||
"# Tampilkan 5 baris pertama\n",
|
||
"print(\"\\nContoh 5 baris data awal:\")\n",
|
||
"display(df_raw.head())\n",
|
||
"\n",
|
||
"# Informasi dasar dataset\n",
|
||
"print(\"\\nInformasi Dataset:\")\n",
|
||
"df_raw.info()\n",
|
||
"\n",
|
||
"# Ubah nama kolom agar konsisten (lowercase)\n",
|
||
"df_raw.columns = [col.lower() for col in df_raw.columns]\n",
|
||
"# Ganti nama 'text' menjadi 'ulasan_asli' untuk kejelasan\n",
|
||
"if 'text' in df_raw.columns:\n",
|
||
" df_raw = df_raw.rename(columns={'text': 'ulasan_asli'})\n",
|
||
"\n",
|
||
"# Cek distribusi label\n",
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"sns.countplot(x='label', data=df_raw, order=df_raw['label'].value_counts().index)\n",
|
||
"plt.title('Distribusi Label Sentimen Awal')\n",
|
||
"plt.xlabel('Sentimen')\n",
|
||
"plt.ylabel('Jumlah Ulasan')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(\"\\nStatistik Distribusi Label:\")\n",
|
||
"print(df_raw['label'].value_counts(normalize=True) * 100)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "46ab111a",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.009073,
|
||
"end_time": "2025-08-17T11:15:26.392318",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:15:26.383245",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"**Temuan Awal:**\n",
|
||
"* Dataset terdiri dari 3506 ulasan.\n",
|
||
"* Terdapat kolom `No`, `Text` (ulasan), dan `Label` (sentimen).\n",
|
||
"* Distribusi kelas terlihat **tidak seimbang (imbalanced)**, dengan sentimen `POSITIF` mendominasi. Ini penting untuk diperhatikan saat pembagian data dan pemilihan metrik evaluasi (Macro F1-score akan lebih cocok daripada akurasi)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "67d76029",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.009481,
|
||
"end_time": "2025-08-17T11:15:26.412514",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:15:26.403033",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 6. Cleaning & Preprocessing Teks\n",
|
||
"\n",
|
||
"Tahap ini menggunakan fungsi pembersihan teks yang sama persis seperti pada notebook awal. Proses ini meliputi penghapusan data yang tidak relevan, *case folding*, normalisasi (kamus alay), penghapusan *stopwords*, dan *stemming* dengan Sastrawi. Hasilnya akan disimpan di kolom baru `text_bersih` yang akan menjadi dasar untuk analisis selanjutnya."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "00c0e7e4",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:15:26.434606Z",
|
||
"iopub.status.busy": "2025-08-17T11:15:26.433445Z",
|
||
"iopub.status.idle": "2025-08-17T11:21:21.120416Z",
|
||
"shell.execute_reply": "2025-08-17T11:21:21.119210Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 354.700031,
|
||
"end_time": "2025-08-17T11:21:21.122293",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:15:26.422262",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Menginisialisasi Stemmer dan Stopwords Sastrawi...\n",
|
||
"Mempersiapkan kamus normalisasi...\n",
|
||
"Jumlah baris awal: 3506\n",
|
||
"Jumlah baris setelah menghapus 'TIDAK RELEVAN': 3452\n",
|
||
"Memulai proses cleaning...\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "bf57afc7c8d24bbdb0d9e5eb9b757a5e",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
" 0%| | 0/3452 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Proses cleaning selesai. Jumlah baris setelah cleaning: 3412\n",
|
||
"Contoh data setelah cleaning:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>ulasan_asli</th>\n",
|
||
" <th>text_bersih</th>\n",
|
||
" <th>label</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Tempat wisata yang bersejarah</td>\n",
|
||
" <td>tempat wisata sejarah</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>belum ke Semarang kalau belum datang ke lawang...</td>\n",
|
||
" <td>semarang kalau datang lawang sewu jawa tengah</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Ternyata ini dulunya gedung percetakan tiket k...</td>\n",
|
||
" <td>nyata gedung cetak tiket kereta api</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>KAI dalam mengelola aset lebih profesional lag...</td>\n",
|
||
" <td>kai kelola aset lebih profesional tiket masuk ...</td>\n",
|
||
" <td>NEGATIF</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Ticket masuk 25.000 per orang.. Air mineral 60...</td>\n",
|
||
" <td>ticket masuk per orang air mineral ml</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" ulasan_asli \\\n",
|
||
"0 Tempat wisata yang bersejarah \n",
|
||
"1 belum ke Semarang kalau belum datang ke lawang... \n",
|
||
"2 Ternyata ini dulunya gedung percetakan tiket k... \n",
|
||
"3 KAI dalam mengelola aset lebih profesional lag... \n",
|
||
"4 Ticket masuk 25.000 per orang.. Air mineral 60... \n",
|
||
"\n",
|
||
" text_bersih label \n",
|
||
"0 tempat wisata sejarah POSITIF \n",
|
||
"1 semarang kalau datang lawang sewu jawa tengah POSITIF \n",
|
||
"2 nyata gedung cetak tiket kereta api NETRAL \n",
|
||
"3 kai kelola aset lebih profesional tiket masuk ... NEGATIF \n",
|
||
"4 ticket masuk per orang air mineral ml NETRAL "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Menginisialisasi Stemmer dan Stopwords Sastrawi...\")\n",
|
||
"factory_stemmer = StemmerFactory()\n",
|
||
"stemmer = factory_stemmer.create_stemmer()\n",
|
||
"factory_stopword = StopWordRemoverFactory()\n",
|
||
"stopwords_sastrawi = factory_stopword.get_stop_words()\n",
|
||
"custom_stopwords = {\n",
|
||
" 'ah', 'je', 'si', 'kat', 'the', 'dah', 'for', 'and', 'nya', 'tuh', 'eh', 'sih', 'dong', 'rt', 'aja', 'kok',\n",
|
||
" 'a', 'an', 'in', 'of', 'is', 'are', 'to', 'with', 'it', 'its', 'was', 'were', 'but', 'so'\n",
|
||
"}\n",
|
||
"all_stopwords = set(stopwords_sastrawi).union(custom_stopwords)\n",
|
||
"\n",
|
||
"print(\"Mempersiapkan kamus normalisasi...\")\n",
|
||
"kamus_normalisasi = {\n",
|
||
" # ==============================================================================\n",
|
||
" # KATEGORI 1: NORMALISASI HARGA, ANGKA & UNIT\n",
|
||
" # Aturan yang menangani format angka, harga, dan satuan.\n",
|
||
" # ==============================================================================\n",
|
||
" r'(\\d+)[.,](\\d{3})': r'\\1\\2', # Menggabungkan angka ribuan: 25.000 -> 25000\n",
|
||
" r'(\\d+)(rb|k)\\b': r'\\1000', # Konversi harga: 20rb/20k -> 20000\n",
|
||
"\n",
|
||
" # ==============================================================================\n",
|
||
" # KATEGORI 2: PENGHAPUSAN KATA\n",
|
||
" # Menghapus kata-kata yang tidak membawa makna (seruan, tawa, dll).\n",
|
||
" # ==============================================================================\n",
|
||
" r'\\b(wkwk|wkwkwk|haha|hehe|xixi)\\b': '', # Hapus kata-kata tertawa\n",
|
||
" r'\\b(bjir|anjay|anjir|anjrit|njir|anjer)\\b': '', # Hapus makian/slang tidak relevan\n",
|
||
"\n",
|
||
" # ==============================================================================\n",
|
||
" # KATEGORI 3: KATA GANTI ORANG (PRONOUNS)\n",
|
||
" # ==============================================================================\n",
|
||
" r'\\b(ak|aq|gw|gue|gua|gwe)\\b': 'aku',\n",
|
||
" r'\\b(km|kamu|loe|elu|lo|elo|lu|kmu|you)\\b': 'kamu',\n",
|
||
" r'\\b(sy|sya)\\b': 'saya',\n",
|
||
"\n",
|
||
" # ==============================================================================\n",
|
||
" # KATEGORI 4: KATA TANYA (QUESTION WORDS)\n",
|
||
" # ==============================================================================\n",
|
||
" r'\\b(gmn|gimana|gmana|gmna|gimanaa)\\b': 'bagaimana',\n",
|
||
" r'\\b(knp|kenap|knpa|kenape)\\b': 'kenapa',\n",
|
||
"\n",
|
||
" # ==============================================================================\n",
|
||
" # KATEGORI 5: KATA KERJA (VERBS)\n",
|
||
" # ==============================================================================\n",
|
||
" r'\\b(blg|blng|blang)\\b': 'bilang',\n",
|
||
" r'\\b(bs|bsa)\\b': 'bisa',\n",
|
||
" r'\\b(cb|cba)\\b': 'coba',\n",
|
||
" r'\\b(dtg)\\b': 'datang',\n",
|
||
" r'\\b(jwb|jwab)\\b': 'jawab',\n",
|
||
" r'\\b(lgsg|lgsung|langsng)\\b': 'langsung',\n",
|
||
" r'\\b(liat|lht|lhat)\\b': 'lihat',\n",
|
||
" r'\\b(lwt|lwat)\\b': 'lewat',\n",
|
||
" r'\\b(mkn|mkan|maem)\\b': 'makan',\n",
|
||
" r'\\b(ovt)\\b': 'overthinking',\n",
|
||
" r'\\b(pake|pk|pke)\\b': 'pakai',\n",
|
||
" r'\\b(tau|th|taw|tauw|tw)\\b': 'tahu',\n",
|
||
" r'\\b(tlg|plis|pls|please|tlong|tlongin)\\b': 'tolong',\n",
|
||
" r'\\b(moga|smoga)\\b': 'semoga',\n",
|
||
"\n",
|
||
" # ==============================================================================\n",
|
||
" # KATEGORI 6: KATA SIFAT & BENDA (ADJECTIVES & NOUNS)\n",
|
||
" # ==============================================================================\n",
|
||
" r'\\b(ank)\\b': 'anak',\n",
|
||
" r'\\b(bgs|bgus|nice|good|top)\\b': 'bagus',\n",
|
||
" r'\\b(bener|bnr|bner)\\b': 'benar',\n",
|
||
" r'\\b(byk|bnyk)\\b': 'banyak',\n",
|
||
" r'\\b(jls|jlas)\\b': 'jelas',\n",
|
||
" r'\\b(josss|joss|sipp|sip)\\b': 'mantap',\n",
|
||
" r'\\b(londo)\\b': 'belanda',\n",
|
||
" r'\\b(org|orng|wong)\\b': 'orang',\n",
|
||
" r'\\b(skt|skit)\\b': 'sakit',\n",
|
||
" r'\\b(syg|syang)\\b': 'sayang',\n",
|
||
" r'\\b(tmpt|tempt|tmpat)\\b': 'tempat', \n",
|
||
" r'\\b(tmn|temen|tmen|tman)\\b': 'teman',\n",
|
||
" r'\\b(lbh|lbih|lb)\\b': 'lebih',\n",
|
||
"\n",
|
||
" # ==============================================================================\n",
|
||
" # KATEGORI 7: KATA SAMBUNG, KETERANGAN, UMUM (CONJUNCTIONS, ADVERBS, ETC)\n",
|
||
" # ==============================================================================\n",
|
||
" r'\\b(aja|sja|aje|sj|ae|wae)\\b': 'saja',\n",
|
||
" r'\\b(bbrp|bbrpa|bberapa)\\b': 'beberapa',\n",
|
||
" r'\\b(bgt|bangettt|bangett|bangt|bget|bbgt)\\b': 'banget', # Dihilangkan ||\n",
|
||
" r'\\b(blm|blom|blum)\\b': 'belum',\n",
|
||
" r'\\b(bkn|bukn)\\b': 'bukan',\n",
|
||
" r'\\b(br|bru)\\b': 'baru',\n",
|
||
" r'\\b(cmn|cuma|cuman|cm)\\b': 'cuma',\n",
|
||
" r'\\b(dgn|dngn|dg)\\b': 'dengan',\n",
|
||
" r'\\b(dl|dlu)\\b': 'dulu',\n",
|
||
" r'\\b(dlm|dalem|dlem)\\b': 'dalam',\n",
|
||
" r'\\b(dr|dri)\\b': 'dari',\n",
|
||
" r'\\b(emg|emng|emang)\\b': 'memang',\n",
|
||
" r'\\b(ga|gak|tdk|gk|ngga|nggak|engga|enggak)\\b': 'tidak',\n",
|
||
" r'\\b(gr|gara2|gr2|gra2|gra|gara)\\b': 'gara-gara',\n",
|
||
" r'\\b(gpp|gppa|gapapa)\\b': 'tidak apa-apa',\n",
|
||
" r'\\b(gt|gitu|bgitu)\\b': 'begitu',\n",
|
||
" r'\\b(hbs|hbis)\\b': 'habis',\n",
|
||
" r'\\b(hrs|hrus)\\b': 'harus',\n",
|
||
" r'\\b(jd|jdi)\\b': 'jadi',\n",
|
||
" r'\\b(jg|jga)\\b': 'juga',\n",
|
||
" r'\\b(jgn|jangan|jngan|jgan)\\b': 'jangan',\n",
|
||
" r'\\b(kdg|kdang)\\b': 'kadang',\n",
|
||
" r'\\b(klo|kalo|kl)\\b': 'kalau',\n",
|
||
" r'\\b(kpd|kpda)\\b': 'kepada',\n",
|
||
" r'\\b(krn|karna)\\b': 'karena',\n",
|
||
" r'\\b(kyk|kek|kaya|kya)\\b': 'seperti',\n",
|
||
" r'\\b(lg|lgi)\\b': 'lagi',\n",
|
||
" r'\\b(mgkn|mngkin|mngkn)\\b': 'mungkin',\n",
|
||
" r'\\b(mlm|mlem)\\b': 'malam',\n",
|
||
" r'\\b(msi|msh|msih)\\b': 'masih',\n",
|
||
" r'\\b(ntr|nnti|ntar)\\b': 'nanti',\n",
|
||
" r'\\b(pd|pda)\\b': 'pada',\n",
|
||
" r'\\b(pny|pnya)\\b': 'punya',\n",
|
||
" r'\\b(prnh|prnah)\\b': 'pernah',\n",
|
||
" r'\\b(sblm|sblom|sblum)\\b': 'sebelum',\n",
|
||
" r'\\b(sbnrnya|sebenernya|sbenernya)\\b': 'sebenarnya',\n",
|
||
" r'\\b(sdg)\\b': 'sedang',\n",
|
||
" r'\\b(sdh|udh|udah|uda)\\b': 'sudah',\n",
|
||
" r'\\b(shg)\\b': 'sehingga',\n",
|
||
" r'\\b(skrg|skrng|skrang)\\b': 'sekarang',\n",
|
||
" r'\\b(sllu|slalu|sll)\\b': 'selalu',\n",
|
||
" r'\\b(sm|ama)\\b': 'sama',\n",
|
||
" r'\\b(smpai|ampe|smp|smpe)\\b': 'sampai',\n",
|
||
" r'\\b(smua)\\b': 'semua',\n",
|
||
" r'\\b(srg|sring)\\b': 'sering',\n",
|
||
" r'\\b(tkt|tkut)\\b': 'takut',\n",
|
||
" r'\\b(tmbh|tmbah)\\b': 'tambah',\n",
|
||
" r'\\b(tntg|ttg|tntang)\\b': 'tentang',\n",
|
||
" r'\\b(tp|tpi)\\b': 'tapi',\n",
|
||
" r'\\b(trll|tralu)\\b': 'terlalu',\n",
|
||
" r'\\b(trmasuk|trmsk|trmsuk)\\b': 'termasuk',\n",
|
||
" r'\\b(trs|trus|truz)\\b': 'lalu',\n",
|
||
" r'\\b(ttp|ttep|ttap)\\b': 'tetap',\n",
|
||
" r'\\b(utk|untk)\\b': 'untuk',\n",
|
||
" r'\\b(yg|yng)\\b': 'yang',\n",
|
||
" r'\\b(supya|spy)\\b': 'supaya',\n",
|
||
" r'\\b(agr)\\b': 'agar',\n",
|
||
" r'\\b(pdhl|pdhal)\\b': 'padahal',\n",
|
||
" \n",
|
||
" # ==============================================================================\n",
|
||
" # KATEGORI 8: SPESIFIK KONTEKS (GOOGLE MAPS, UMUM)\n",
|
||
" # ==============================================================================\n",
|
||
" r'bareng\\s*yu+': 'bareng ayo', # Kasus khusus tanpa word boundary\n",
|
||
" r'\\b(gopay|qris|cash)\\b': 'pembayaran',\n",
|
||
" r'\\b(healing)\\b': 'rekreasi',\n",
|
||
" r'\\b(htm)\\b': 'harga tiket masuk',\n",
|
||
" r'\\b(overall)\\b': 'secara keseluruhan',\n",
|
||
" r'\\b(photo?booth)\\b': 'photobooth',\n",
|
||
" r'\\b(recommended|recomended|recomsnded)\\b': 'rekomendasi',\n",
|
||
" r'\\b(smgt|smgat|smngat|smangat)\\b': 'semangat',\n",
|
||
" r'\\b(spot foto)\\b': 'lokasi foto',\n",
|
||
" r'\\b(thx|tq|makasi|makasih|makasii|makasie|thanks|thank|thengs)\\b': 'terima kasih',\n",
|
||
" r'\\b(tour guide)\\b': 'pemandu wisata',\n",
|
||
" r'\\b(view)\\b': 'pemandangan',\n",
|
||
" r'\\b(vintage)\\b': 'antik',\n",
|
||
" r'\\b(weekend)\\b': 'akhir pekan',\n",
|
||
" r'\\b(weekdays)\\b': 'hari kerja',\n",
|
||
" r'\\b(yuk|yuu|ayok|ayoo|ayuk)\\b': 'ayo',\n",
|
||
" r'\\b(sblh|sebelh|sblah)\\b': 'sebelah',\n",
|
||
" r'\\b(kyknya|kayaknya)\\b': 'kemungkinan',\n",
|
||
"}\n",
|
||
"\n",
|
||
"print(f\"Jumlah baris awal: {len(df_raw)}\")\n",
|
||
"df_raw = df_raw[df_raw['label'] != 'TIDAK RELEVAN'].copy()\n",
|
||
"print(f\"Jumlah baris setelah menghapus 'TIDAK RELEVAN': {len(df_raw)}\")\n",
|
||
"\n",
|
||
"def bersihkan_teks(teks):\n",
|
||
" if not isinstance(teks, str):\n",
|
||
" return \"\"\n",
|
||
" \n",
|
||
" teks = teks.lower()\n",
|
||
" teks = re.sub(r'https?://\\S+|www\\.\\S+', '', teks)\n",
|
||
" teks = re.sub(r'@\\w+|#\\w+', '', teks)\n",
|
||
" teks = re.sub(r'[^\\w\\s]', ' ', teks)\n",
|
||
" for pattern, replacement in kamus_normalisasi.items():\n",
|
||
" teks = re.sub(pattern, replacement, teks)\n",
|
||
" teks = re.sub(r'\\d+', '', teks)\n",
|
||
" teks = re.sub(r'(\\w)\\1{2,}', r'\\1', teks)\n",
|
||
" words = teks.split()\n",
|
||
" stemmed_words = [stemmer.stem(word) for word in words]\n",
|
||
" teks = ' '.join(stemmed_words)\n",
|
||
" words = teks.split()\n",
|
||
" filtered_words = [word for word in words if word not in all_stopwords and len(word) > 1]\n",
|
||
" teks = ' '.join(filtered_words)\n",
|
||
" teks = re.sub(r'\\s+', ' ', teks).strip()\n",
|
||
" return teks\n",
|
||
"\n",
|
||
"print(\"Memulai proses cleaning...\")\n",
|
||
"df_raw['text_bersih'] = df_raw['ulasan_asli'].progress_apply(bersihkan_teks)\n",
|
||
"\n",
|
||
"# Hapus baris yang teksnya menjadi kosong setelah cleaning\n",
|
||
"df_clean = df_raw[df_raw['text_bersih'].str.len() > 0].copy()\n",
|
||
"\n",
|
||
"print(f\"\\nProses cleaning selesai. Jumlah baris setelah cleaning: {len(df_clean)}\")\n",
|
||
"print(\"Contoh data setelah cleaning:\")\n",
|
||
"display(df_clean[['ulasan_asli', 'text_bersih', 'label']].head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "56479295",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.010113,
|
||
"end_time": "2025-08-17T11:21:21.143263",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.133150",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 7. Deduplikasi Konten\n",
|
||
"\n",
|
||
"Untuk menghindari bias pada model, ulasan yang identik atau sangat mirip perlu dihapus. Kita akan melakukan dua jenis deduplikasi:\n",
|
||
"1. **Exact Deduplication**: Menghapus baris yang memiliki `text_bersih` yang sama persis.\n",
|
||
"2. **Near-Duplicate Detection (Opsional)**: Menggunakan rasio kemiripan token untuk menyaring ulasan yang substansinya sama meskipun ada sedikit perbedaan (misal: tambahan tanda baca atau satu kata).\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "c64fe2d9",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:21:21.164090Z",
|
||
"iopub.status.busy": "2025-08-17T11:21:21.163751Z",
|
||
"iopub.status.idle": "2025-08-17T11:21:21.177483Z",
|
||
"shell.execute_reply": "2025-08-17T11:21:21.176107Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.026499,
|
||
"end_time": "2025-08-17T11:21:21.179294",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.152795",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Jumlah baris sebelum deduplikasi: 3412\n",
|
||
"Jumlah baris setelah deduplikasi eksak: 3074\n",
|
||
"\n",
|
||
"Deteksi near-duplicate dilewati.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f\"Jumlah baris sebelum deduplikasi: {len(df_clean)}\")\n",
|
||
"\n",
|
||
"# 1. Exact Deduplication\n",
|
||
"df_dedup = df_clean.drop_duplicates(subset=['text_bersih'], keep='first').copy()\n",
|
||
"print(f\"Jumlah baris setelah deduplikasi eksak: {len(df_dedup)}\")\n",
|
||
"\n",
|
||
"# 2. Near-Duplicate Detection\n",
|
||
"# Proses ini bisa lambat, jadi kita lakukan pada sampel kecil atau jika diperlukan\n",
|
||
"duplikat_indeks = []\n",
|
||
"teks_list = df_dedup['text_bersih'].tolist()\n",
|
||
"indeks_list = df_dedup.index.tolist()\n",
|
||
"\n",
|
||
"# (Langkah ini dinonaktifkan secara default karena komputasinya berat, aktifkan jika perlu)\n",
|
||
"RUN_NEAR_DEDUPLICATION = False\n",
|
||
"\n",
|
||
"if RUN_NEAR_DEDUPLICATION:\n",
|
||
" print(\"\\nMemulai deteksi near-duplicate (mungkin butuh waktu lama)...\")\n",
|
||
" for i in tqdm(range(len(teks_list))):\n",
|
||
" if indeks_list[i] in duplikat_indeks:\n",
|
||
" continue\n",
|
||
" for j in range(i + 1, len(teks_list)):\n",
|
||
" if indeks_list[j] in duplikat_indeks:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" rasio_kemiripan = fuzz.token_set_ratio(teks_list[i], teks_list[j])\n",
|
||
" if rasio_kemiripan >= 95: # Ambang batas kemiripan\n",
|
||
" duplikat_indeks.append(indeks_list[j])\n",
|
||
"\n",
|
||
" df_final = df_dedup.drop(index=duplikat_indeks)\n",
|
||
" print(f\"Jumlah baris setelah deteksi near-duplicate: {len(df_final)}\")\n",
|
||
"else:\n",
|
||
" df_final = df_dedup.copy()\n",
|
||
" print(\"\\nDeteksi near-duplicate dilewati.\")\n",
|
||
"\n",
|
||
"df_final.reset_index(drop=True, inplace=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c0b968a6",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.010419,
|
||
"end_time": "2025-08-17T11:21:21.200867",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.190448",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 8. Pembagian Data (Stratified Train/Val/Test Split)\n",
|
||
"\n",
|
||
"Dataset akan dibagi menjadi tiga bagian: **latih (70%)**, **validasi (15%)**, dan **uji (15%)**. Kita menggunakan *stratified split* berdasarkan kolom `label` untuk memastikan proporsi setiap kelas sentimen sama di ketiga set. Ini sangat penting untuk dataset yang tidak seimbang agar evaluasi model menjadi adil dan tidak bias."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "64aa7911",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:21:21.223224Z",
|
||
"iopub.status.busy": "2025-08-17T11:21:21.222859Z",
|
||
"iopub.status.idle": "2025-08-17T11:21:21.249600Z",
|
||
"shell.execute_reply": "2025-08-17T11:21:21.248465Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.040721,
|
||
"end_time": "2025-08-17T11:21:21.251892",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.211171",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Ukuran data Latih (Train): 2151 (70.0%)\n",
|
||
"Ukuran data Validasi (Val): 461 (15.0%)\n",
|
||
"Ukuran data Uji (Test): 462 (15.0%)\n",
|
||
"\n",
|
||
"Distribusi label di setiap set:\n",
|
||
"Train:\n",
|
||
" label\n",
|
||
"POSITIF 0.716411\n",
|
||
"NETRAL 0.154812\n",
|
||
"NEGATIF 0.128777\n",
|
||
"Name: proportion, dtype: float64\n",
|
||
"\n",
|
||
"Validation:\n",
|
||
" label\n",
|
||
"POSITIF 0.718004\n",
|
||
"NETRAL 0.154013\n",
|
||
"NEGATIF 0.127983\n",
|
||
"Name: proportion, dtype: float64\n",
|
||
"\n",
|
||
"Test:\n",
|
||
" label\n",
|
||
"POSITIF 0.716450\n",
|
||
"NETRAL 0.155844\n",
|
||
"NEGATIF 0.127706\n",
|
||
"Name: proportion, dtype: float64\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:605: DeprecationWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
|
||
" if is_sparse(pd_dtype):\n",
|
||
"/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:614: DeprecationWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
|
||
" if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n",
|
||
"/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:605: DeprecationWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
|
||
" if is_sparse(pd_dtype):\n",
|
||
"/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:614: DeprecationWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
|
||
" if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"X = df_final['text_bersih']\n",
|
||
"y = df_final['label']\n",
|
||
"\n",
|
||
"# Bagi menjadi train+val (85%) dan test (15%)\n",
|
||
"X_train_val, X_test, y_train_val, y_test = train_test_split(\n",
|
||
" X, y, test_size=0.15, random_state=SEED, stratify=y\n",
|
||
")\n",
|
||
"\n",
|
||
"# Bagi train+val menjadi train (70%) dan val (15%)\n",
|
||
"# Proporsi test_size dihitung dari sisa data (0.15 / 0.85)\n",
|
||
"X_train, X_val, y_train, y_val = train_test_split(\n",
|
||
" X_train_val, y_train_val, test_size=(0.15/0.85), random_state=SEED, stratify=y_train_val\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"Ukuran data Latih (Train): {len(X_train)} ({len(X_train)/len(df_final)*100:.1f}%)\")\n",
|
||
"print(f\"Ukuran data Validasi (Val): {len(X_val)} ({len(X_val)/len(df_final)*100:.1f}%)\")\n",
|
||
"print(f\"Ukuran data Uji (Test): {len(X_test)} ({len(X_test)/len(df_final)*100:.1f}%)\")\n",
|
||
"\n",
|
||
"print(\"\\nDistribusi label di setiap set:\")\n",
|
||
"print(\"Train:\\n\", y_train.value_counts(normalize=True))\n",
|
||
"print(\"\\nValidation:\\n\", y_val.value_counts(normalize=True))\n",
|
||
"print(\"\\nTest:\\n\", y_test.value_counts(normalize=True))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c524ca86",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.010854,
|
||
"end_time": "2025-08-17T11:21:21.273236",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.262382",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9d13ccc2",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.010408,
|
||
"end_time": "2025-08-17T11:21:21.294382",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.283974",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"## Bagian I: Analisis Sentimen (Supervised)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f6867b49",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.009691,
|
||
"end_time": "2025-08-17T11:21:21.314133",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.304442",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 10. Representasi Fitur (TF-IDF)\n",
|
||
"\n",
|
||
"Kita akan mengubah teks menjadi vektor numerik menggunakan **TF-IDF (Term Frequency-Inverse Document Frequency)**. Metode ini memberi bobot lebih tinggi pada kata-kata yang sering muncul dalam satu dokumen tetapi jarang muncul di seluruh korpus, sehingga efektif menangkap kata kunci penting.\n",
|
||
"\n",
|
||
"**Konfigurasi:**\n",
|
||
"- `ngram_range=(1, 2)`: Menggunakan unigram (satu kata) dan bigram (dua kata) untuk menangkap konteks.\n",
|
||
"- `min_df=3`: Mengabaikan kata yang muncul kurang dari 3 kali di seluruh korpus untuk mengurangi noise.\n",
|
||
"- `max_df=0.9`: Mengabaikan kata yang muncul di lebih dari 90% dokumen (kata terlalu umum).\n",
|
||
"- `sublinear_tf=True`: Menerapkan penskalaan logaritmik pada frekuensi term."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "b7a79eea",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:21:21.336997Z",
|
||
"iopub.status.busy": "2025-08-17T11:21:21.335525Z",
|
||
"iopub.status.idle": "2025-08-17T11:21:21.499465Z",
|
||
"shell.execute_reply": "2025-08-17T11:21:21.498212Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.17688,
|
||
"end_time": "2025-08-17T11:21:21.501123",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.324243",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Ukuran matriks TF-IDF (data latih): (2151, 2297)\n",
|
||
"Jumlah fitur (kosakata): 2297\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"tfidf = TfidfVectorizer(\n",
|
||
" ngram_range=(1, 2),\n",
|
||
" min_df=3,\n",
|
||
" max_df=0.9,\n",
|
||
" sublinear_tf=True,\n",
|
||
" # random_state=SEED\n",
|
||
")\n",
|
||
"\n",
|
||
"# Fit pada data latih, lalu transform semua set\n",
|
||
"X_train_tfidf = tfidf.fit_transform(X_train)\n",
|
||
"X_val_tfidf = tfidf.transform(X_val)\n",
|
||
"X_test_tfidf = tfidf.transform(X_test)\n",
|
||
"\n",
|
||
"print(f\"Ukuran matriks TF-IDF (data latih): {X_train_tfidf.shape}\")\n",
|
||
"print(f\"Jumlah fitur (kosakata): {len(tfidf.get_feature_names_out())}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "80ebe1bc",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.009333,
|
||
"end_time": "2025-08-17T11:21:21.520252",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:21:21.510919",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 11. Screening Cepat (LazyPredict) – Opsional\n",
|
||
"\n",
|
||
"Sebelum melakukan analisis mendalam pada beberapa model, kita akan menggunakan `LazyPredict` untuk menjalankan puluhan model klasifikasi secara otomatis. Tujuannya adalah untuk mendapatkan gambaran umum tentang algoritma mana yang paling menjanjikan untuk dataset ini tanpa perlu melakukan *tuning*.\n",
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"\n",
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"**Catatan:** Hasil dari LazyPredict adalah **panduan awal**, bukan hasil final. Kita akan memilih beberapa model teratas dari sini untuk dianalisis lebih lanjut."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "e905dd4b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2025-08-17T11:21:21.542709Z",
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"iopub.status.busy": "2025-08-17T11:21:21.542283Z",
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"iopub.status.idle": "2025-08-17T11:26:00.287968Z",
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"shell.execute_reply": "2025-08-17T11:26:00.286805Z"
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},
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"papermill": {
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"duration": 278.759203,
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"end_time": "2025-08-17T11:26:00.289725",
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"exception": false,
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"start_time": "2025-08-17T11:21:21.530522",
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"status": "completed"
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},
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[?25hInstalling collected packages: pytest-runner, gunicorn, graphql-core, opentelemetry-api, graphql-relay, opentelemetry-semantic-conventions, graphene, databricks-sdk, opentelemetry-sdk, mlflow-tracing, mlflow-skinny, mlflow, lazypredict\r\n",
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"Successfully installed databricks-sdk-0.63.0 graphene-3.4.3 graphql-core-3.2.6 graphql-relay-3.2.0 gunicorn-23.0.0 lazypredict-0.2.16 mlflow-3.2.0 mlflow-skinny-3.2.0 mlflow-tracing-3.2.0 opentelemetry-api-1.36.0 opentelemetry-sdk-1.36.0 opentelemetry-semantic-conventions-0.57b0 pytest-runner-6.0.1\r\n",
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"Menjalankan LazyPredict pada data train dan test...\n"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "72f955b0813b49c5817f287c5ac0aac5",
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004125 seconds.\n",
|
||
"You can set `force_col_wise=true` to remove the overhead.\n",
|
||
"[LightGBM] [Info] Total Bins 6727\n",
|
||
"[LightGBM] [Info] Number of data points in the train set: 2151, number of used features: 292\n",
|
||
"[LightGBM] [Info] Start training from score -2.049671\n",
|
||
"[LightGBM] [Info] Start training from score -1.865546\n",
|
||
"[LightGBM] [Info] Start training from score -0.333501\n",
|
||
"\n",
|
||
"Hasil Screening Model dengan LazyPredict (diurutkan berdasarkan F1 Score):\n"
|
||
]
|
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},
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{
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"data": {
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"text/html": [
|
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" .dataframe tbody tr th:only-of-type {\n",
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|
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Accuracy</th>\n",
|
||
" <th>Balanced Accuracy</th>\n",
|
||
" <th>ROC AUC</th>\n",
|
||
" <th>F1 Score</th>\n",
|
||
" <th>Time Taken</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Model</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>NearestCentroid</th>\n",
|
||
" <td>0.77</td>\n",
|
||
" <td>0.63</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.77</td>\n",
|
||
" <td>0.37</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>LGBMClassifier</th>\n",
|
||
" <td>0.77</td>\n",
|
||
" <td>0.57</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.75</td>\n",
|
||
" <td>1.35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>LogisticRegression</th>\n",
|
||
" <td>0.75</td>\n",
|
||
" <td>0.59</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.74</td>\n",
|
||
" <td>2.49</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>RandomForestClassifier</th>\n",
|
||
" <td>0.77</td>\n",
|
||
" <td>0.52</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.73</td>\n",
|
||
" <td>4.31</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>ExtraTreesClassifier</th>\n",
|
||
" <td>0.76</td>\n",
|
||
" <td>0.52</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.73</td>\n",
|
||
" <td>11.97</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>PassiveAggressiveClassifier</th>\n",
|
||
" <td>0.72</td>\n",
|
||
" <td>0.59</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.72</td>\n",
|
||
" <td>1.75</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>BaggingClassifier</th>\n",
|
||
" <td>0.73</td>\n",
|
||
" <td>0.57</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.72</td>\n",
|
||
" <td>12.21</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>AdaBoostClassifier</th>\n",
|
||
" <td>0.74</td>\n",
|
||
" <td>0.50</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.70</td>\n",
|
||
" <td>4.63</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>LinearSVC</th>\n",
|
||
" <td>0.69</td>\n",
|
||
" <td>0.57</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.70</td>\n",
|
||
" <td>35.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>BernoulliNB</th>\n",
|
||
" <td>0.73</td>\n",
|
||
" <td>0.49</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.70</td>\n",
|
||
" <td>0.43</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>SGDClassifier</th>\n",
|
||
" <td>0.75</td>\n",
|
||
" <td>0.45</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.69</td>\n",
|
||
" <td>1.54</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DecisionTreeClassifier</th>\n",
|
||
" <td>0.69</td>\n",
|
||
" <td>0.54</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.69</td>\n",
|
||
" <td>2.27</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Perceptron</th>\n",
|
||
" <td>0.69</td>\n",
|
||
" <td>0.53</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.69</td>\n",
|
||
" <td>1.07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>ExtraTreeClassifier</th>\n",
|
||
" <td>0.70</td>\n",
|
||
" <td>0.48</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.68</td>\n",
|
||
" <td>0.41</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>KNeighborsClassifier</th>\n",
|
||
" <td>0.71</td>\n",
|
||
" <td>0.40</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.65</td>\n",
|
||
" <td>0.53</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>RidgeClassifierCV</th>\n",
|
||
" <td>0.63</td>\n",
|
||
" <td>0.49</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.64</td>\n",
|
||
" <td>2.61</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>SVC</th>\n",
|
||
" <td>0.73</td>\n",
|
||
" <td>0.38</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.63</td>\n",
|
||
" <td>14.50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>CalibratedClassifierCV</th>\n",
|
||
" <td>0.73</td>\n",
|
||
" <td>0.37</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.63</td>\n",
|
||
" <td>148.45</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>RidgeClassifier</th>\n",
|
||
" <td>0.60</td>\n",
|
||
" <td>0.47</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.62</td>\n",
|
||
" <td>0.89</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>DummyClassifier</th>\n",
|
||
" <td>0.72</td>\n",
|
||
" <td>0.33</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.60</td>\n",
|
||
" <td>0.33</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>GaussianNB</th>\n",
|
||
" <td>0.55</td>\n",
|
||
" <td>0.48</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.58</td>\n",
|
||
" <td>0.45</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>LinearDiscriminantAnalysis</th>\n",
|
||
" <td>0.52</td>\n",
|
||
" <td>0.43</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.55</td>\n",
|
||
" <td>5.51</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>QuadraticDiscriminantAnalysis</th>\n",
|
||
" <td>0.25</td>\n",
|
||
" <td>0.38</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.26</td>\n",
|
||
" <td>3.12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>LabelSpreading</th>\n",
|
||
" <td>0.15</td>\n",
|
||
" <td>0.34</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.08</td>\n",
|
||
" <td>1.32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>LabelPropagation</th>\n",
|
||
" <td>0.15</td>\n",
|
||
" <td>0.34</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>0.08</td>\n",
|
||
" <td>1.26</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Accuracy Balanced Accuracy ROC AUC F1 Score \\\n",
|
||
"Model \n",
|
||
"NearestCentroid 0.77 0.63 None 0.77 \n",
|
||
"LGBMClassifier 0.77 0.57 None 0.75 \n",
|
||
"LogisticRegression 0.75 0.59 None 0.74 \n",
|
||
"RandomForestClassifier 0.77 0.52 None 0.73 \n",
|
||
"ExtraTreesClassifier 0.76 0.52 None 0.73 \n",
|
||
"PassiveAggressiveClassifier 0.72 0.59 None 0.72 \n",
|
||
"BaggingClassifier 0.73 0.57 None 0.72 \n",
|
||
"AdaBoostClassifier 0.74 0.50 None 0.70 \n",
|
||
"LinearSVC 0.69 0.57 None 0.70 \n",
|
||
"BernoulliNB 0.73 0.49 None 0.70 \n",
|
||
"SGDClassifier 0.75 0.45 None 0.69 \n",
|
||
"DecisionTreeClassifier 0.69 0.54 None 0.69 \n",
|
||
"Perceptron 0.69 0.53 None 0.69 \n",
|
||
"ExtraTreeClassifier 0.70 0.48 None 0.68 \n",
|
||
"KNeighborsClassifier 0.71 0.40 None 0.65 \n",
|
||
"RidgeClassifierCV 0.63 0.49 None 0.64 \n",
|
||
"SVC 0.73 0.38 None 0.63 \n",
|
||
"CalibratedClassifierCV 0.73 0.37 None 0.63 \n",
|
||
"RidgeClassifier 0.60 0.47 None 0.62 \n",
|
||
"DummyClassifier 0.72 0.33 None 0.60 \n",
|
||
"GaussianNB 0.55 0.48 None 0.58 \n",
|
||
"LinearDiscriminantAnalysis 0.52 0.43 None 0.55 \n",
|
||
"QuadraticDiscriminantAnalysis 0.25 0.38 None 0.26 \n",
|
||
"LabelSpreading 0.15 0.34 None 0.08 \n",
|
||
"LabelPropagation 0.15 0.34 None 0.08 \n",
|
||
"\n",
|
||
" Time Taken \n",
|
||
"Model \n",
|
||
"NearestCentroid 0.37 \n",
|
||
"LGBMClassifier 1.35 \n",
|
||
"LogisticRegression 2.49 \n",
|
||
"RandomForestClassifier 4.31 \n",
|
||
"ExtraTreesClassifier 11.97 \n",
|
||
"PassiveAggressiveClassifier 1.75 \n",
|
||
"BaggingClassifier 12.21 \n",
|
||
"AdaBoostClassifier 4.63 \n",
|
||
"LinearSVC 35.00 \n",
|
||
"BernoulliNB 0.43 \n",
|
||
"SGDClassifier 1.54 \n",
|
||
"DecisionTreeClassifier 2.27 \n",
|
||
"Perceptron 1.07 \n",
|
||
"ExtraTreeClassifier 0.41 \n",
|
||
"KNeighborsClassifier 0.53 \n",
|
||
"RidgeClassifierCV 2.61 \n",
|
||
"SVC 14.50 \n",
|
||
"CalibratedClassifierCV 148.45 \n",
|
||
"RidgeClassifier 0.89 \n",
|
||
"DummyClassifier 0.33 \n",
|
||
"GaussianNB 0.45 \n",
|
||
"LinearDiscriminantAnalysis 5.51 \n",
|
||
"QuadraticDiscriminantAnalysis 3.12 \n",
|
||
"LabelSpreading 1.32 \n",
|
||
"LabelPropagation 1.26 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install lazypredict # Jalankan baris ini jika Anda belum menginstal lazypredict\n",
|
||
"\n",
|
||
"from lazypredict.Supervised import LazyClassifier\n",
|
||
"\n",
|
||
"# LazyPredict membutuhkan matriks dense, yang bisa memakan banyak memori.\n",
|
||
"# Jika terjadi error memori, pertimbangkan untuk menjalankan pada sampel data yang lebih kecil.\n",
|
||
"print(\"Menjalankan LazyPredict pada data train dan test...\")\n",
|
||
"try:\n",
|
||
" # Inisialisasi LazyClassifier\n",
|
||
" clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)\n",
|
||
" \n",
|
||
" # Fit model. LazyPredict menggunakan split train/test untuk evaluasi cepatnya.\n",
|
||
" models, predictions = clf.fit(X_train_tfidf.toarray(), X_test_tfidf.toarray(), y_train, y_test)\n",
|
||
" \n",
|
||
" print(\"\\nHasil Screening Model dengan LazyPredict (diurutkan berdasarkan F1 Score):\")\n",
|
||
" display(models.sort_values(by='F1 Score', ascending=False))\n",
|
||
"\n",
|
||
"except MemoryError:\n",
|
||
" print(\"\\nERROR: Terjadi MemoryError. Matriks TF-IDF terlalu besar untuk diubah menjadi array dense.\")\n",
|
||
" print(\"Screening dengan LazyPredict dilewati. Lanjut ke validasi manual.\")\n",
|
||
"except Exception as e:\n",
|
||
" print(f\"\\nTerjadi error saat menjalankan LazyPredict: {e}\")\n",
|
||
" print(\"Screening dengan LazyPredict dilewati. Lanjut ke validasi manual.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c8d4070d",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.012834,
|
||
"end_time": "2025-08-17T11:26:00.315994",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:00.303160",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 12. Kandidat Model & Cross-Validation\n",
|
||
"\n",
|
||
"Berdasarkan hasil screening dari LazyPredict, model berbasis ensemble tree seperti **LGBMClassifier**, **NearestCentroid** dan **RandomForestClassifier** menunjukkan performa F1-Score tertinggi. Oleh karena itu, kita akan fokus pada kedua model ini dan mengevaluasinya secara lebih teliti menggunakan **Stratified 5-Fold Cross-Validation**.\n",
|
||
"\n",
|
||
"Metrik utama yang digunakan adalah **Macro F1-score** karena lebih adil untuk dataset yang tidak seimbang."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "bd700f90",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:26:00.343841Z",
|
||
"iopub.status.busy": "2025-08-17T11:26:00.343406Z",
|
||
"iopub.status.idle": "2025-08-17T11:26:09.119998Z",
|
||
"shell.execute_reply": "2025-08-17T11:26:09.118939Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 8.79228,
|
||
"end_time": "2025-08-17T11:26:09.121516",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:00.329236",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Menjalankan 5-Fold Cross Validation pada model kandidat...\n",
|
||
"NearestCentroid: Macro F1 = 0.6013 (+/- 0.0205)\n",
|
||
"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004869 seconds.\n",
|
||
"You can set `force_row_wise=true` to remove the overhead.\n",
|
||
"And if memory is not enough, you can set `force_col_wise=true`.\n",
|
||
"[LightGBM] [Info] Total Bins 6420\n",
|
||
"[LightGBM] [Info] Number of data points in the train set: 2089, number of used features: 287\n",
|
||
"[LightGBM] [Info] Start training from score -2.053454\n",
|
||
"[LightGBM] [Info] Start training from score -1.866788\n",
|
||
"[LightGBM] [Info] Start training from score -0.332555\n",
|
||
"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003714 seconds.\n",
|
||
"You can set `force_row_wise=true` to remove the overhead.\n",
|
||
"And if memory is not enough, you can set `force_col_wise=true`.\n",
|
||
"[LightGBM] [Info] Total Bins 6464\n",
|
||
"[LightGBM] [Info] Number of data points in the train set: 2089, number of used features: 296\n",
|
||
"[LightGBM] [Info] Start training from score -2.049729\n",
|
||
"[LightGBM] [Info] Start training from score -1.866788\n",
|
||
"[LightGBM] [Info] Start training from score -0.333222\n",
|
||
"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003591 seconds.\n",
|
||
"You can set `force_row_wise=true` to remove the overhead.\n",
|
||
"And if memory is not enough, you can set `force_col_wise=true`.\n",
|
||
"[LightGBM] [Info] Total Bins 6393\n",
|
||
"[LightGBM] [Info] Number of data points in the train set: 2090, number of used features: 291\n",
|
||
"[LightGBM] [Info] Start training from score -2.050208\n",
|
||
"[LightGBM] [Info] Start training from score -1.864176\n",
|
||
"[LightGBM] [Info] Start training from score -0.333701\n",
|
||
"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003440 seconds.\n",
|
||
"You can set `force_row_wise=true` to remove the overhead.\n",
|
||
"And if memory is not enough, you can set `force_col_wise=true`.\n",
|
||
"[LightGBM] [Info] Total Bins 6236\n",
|
||
"[LightGBM] [Info] Number of data points in the train set: 2090, number of used features: 285\n",
|
||
"[LightGBM] [Info] Start training from score -2.050208\n",
|
||
"[LightGBM] [Info] Start training from score -1.867267\n",
|
||
"[LightGBM] [Info] Start training from score -0.333033\n",
|
||
"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003324 seconds.\n",
|
||
"You can set `force_row_wise=true` to remove the overhead.\n",
|
||
"And if memory is not enough, you can set `force_col_wise=true`.\n",
|
||
"[LightGBM] [Info] Total Bins 6283\n",
|
||
"[LightGBM] [Info] Number of data points in the train set: 2090, number of used features: 286\n",
|
||
"[LightGBM] [Info] Start training from score -2.050208\n",
|
||
"[LightGBM] [Info] Start training from score -1.867267\n",
|
||
"[LightGBM] [Info] Start training from score -0.333033\n",
|
||
"LGBMClassifier: Macro F1 = 0.5919 (+/- 0.0243)\n",
|
||
"RandomForestClassifier: Macro F1 = 0.5439 (+/- 0.0180)\n",
|
||
"\n",
|
||
"Hasil Cross-Validation (diurutkan berdasarkan Mean Macro F1):\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>mean_f1</th>\n",
|
||
" <th>std_f1</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>NearestCentroid</th>\n",
|
||
" <td>0.60</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>LGBMClassifier</th>\n",
|
||
" <td>0.59</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>RandomForestClassifier</th>\n",
|
||
" <td>0.54</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" mean_f1 std_f1\n",
|
||
"NearestCentroid 0.60 0.02\n",
|
||
"LGBMClassifier 0.59 0.02\n",
|
||
"RandomForestClassifier 0.54 0.02"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Gabungkan kembali data latih dan validasi untuk Cross-Validation\n",
|
||
"X_combined = pd.concat([X_train, X_val])\n",
|
||
"y_combined = pd.concat([y_train, y_val])\n",
|
||
"\n",
|
||
"models = {\n",
|
||
" 'NearestCentroid': NearestCentroid(), \n",
|
||
" 'LGBMClassifier': LGBMClassifier(random_state=SEED),\n",
|
||
" 'RandomForestClassifier': RandomForestClassifier(random_state=SEED, n_jobs=-1) # n_jobs=-1 untuk mempercepat training\n",
|
||
"}\n",
|
||
"\n",
|
||
"results = {}\n",
|
||
"kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)\n",
|
||
"\n",
|
||
"print(\"Menjalankan 5-Fold Cross Validation pada model kandidat...\")\n",
|
||
"for name, model in models.items():\n",
|
||
" # Buat pipeline untuk setiap model agar konsisten\n",
|
||
" pipeline = Pipeline([\n",
|
||
" ('tfidf', TfidfVectorizer(ngram_range=(1,2), min_df=3, max_df=0.9, sublinear_tf=True)),\n",
|
||
" ('clf', model)\n",
|
||
" ])\n",
|
||
" \n",
|
||
" cv_scores = cross_val_score(pipeline, X_combined, y_combined, cv=kfold, scoring='f1_macro')\n",
|
||
" results[name] = {\n",
|
||
" 'mean_f1': cv_scores.mean(),\n",
|
||
" 'std_f1': cv_scores.std()\n",
|
||
" }\n",
|
||
" print(f\"{name}: Macro F1 = {cv_scores.mean():.4f} (+/- {cv_scores.std():.4f})\")\n",
|
||
"\n",
|
||
"results_df = pd.DataFrame(results).T.sort_values(by='mean_f1', ascending=False)\n",
|
||
"print(\"\\nHasil Cross-Validation (diurutkan berdasarkan Mean Macro F1):\")\n",
|
||
"display(results_df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2b4f195d",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.013373,
|
||
"end_time": "2025-08-17T11:26:09.148889",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:09.135516",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"**Temuan:**\n",
|
||
"**NearestCentroid\t** secara konsisten menunjukkan performa terbaik dengan rata-rata Macro F1-score tertinggi walaupun standar deviasinya sama jika dibandingkan dengan **RandomForestCLassifier** maupun **LGBMClassifier**, menjadikannya kandidat utama untuk *hyperparameter tuning*."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a1d1ba45",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.01322,
|
||
"end_time": "2025-08-17T11:26:09.175849",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:09.162629",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 13. Tuning Hiperparameter (Grid Search)\n",
|
||
"\n",
|
||
"Sekarang kita akan melakukan pencarian hiperparameter terbaik untuk `NearestCentroid` menggunakan `GridSearchCV`. Kita akan mencari kombinasi parameter TF-IDF dan parameter `C` (regularisasi) dari NearestCentroid yang memberikan Macro F1-score tertinggi pada data validasi."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "17ac210e",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:26:09.204468Z",
|
||
"iopub.status.busy": "2025-08-17T11:26:09.204140Z",
|
||
"iopub.status.idle": "2025-08-17T11:26:22.313171Z",
|
||
"shell.execute_reply": "2025-08-17T11:26:22.312146Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 13.125571,
|
||
"end_time": "2025-08-17T11:26:22.315110",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:09.189539",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Memulai Grid Search untuk NearestCentroid...\n",
|
||
"Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
|
||
"\n",
|
||
"Parameter Terbaik: {'clf__metric': 'euclidean', 'clf__shrink_threshold': None, 'tfidf__min_df': 5, 'tfidf__ngram_range': (1, 2), 'tfidf__sublinear_tf': True}\n",
|
||
"Skor Macro F1-score terbaik di CV: 0.5958\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1. Definisikan pipeline dengan model yang benar\n",
|
||
"pipeline = Pipeline([\n",
|
||
" ('tfidf', TfidfVectorizer()),\n",
|
||
" ('clf', NearestCentroid()) \n",
|
||
"])\n",
|
||
"\n",
|
||
"# 2. Buat parameter grid yang sesuai untuk NearestCentroid dan TfidfVectorizer\n",
|
||
"param_grid = {\n",
|
||
" 'tfidf__ngram_range': [(1, 1), (1, 2)],\n",
|
||
" 'tfidf__min_df': [2, 3, 5],\n",
|
||
" 'tfidf__sublinear_tf': [True, False],\n",
|
||
" 'clf__metric': ['euclidean', 'manhattan'], \n",
|
||
" 'clf__shrink_threshold': [None, 0.1, 0.5] \n",
|
||
"}\n",
|
||
"\n",
|
||
"# 3. Jalankan GridSearchCV seperti sebelumnya\n",
|
||
"grid_search = GridSearchCV(pipeline, param_grid, cv=kfold, scoring='f1_macro', n_jobs=-1, verbose=1)\n",
|
||
"\n",
|
||
"print(\"Memulai Grid Search untuk NearestCentroid...\")\n",
|
||
"grid_search.fit(X_train_val, y_train_val)\n",
|
||
"\n",
|
||
"print(f\"\\nParameter Terbaik: {grid_search.best_params_}\")\n",
|
||
"print(f\"Skor Macro F1-score terbaik di CV: {grid_search.best_score_:.4f}\")\n",
|
||
"\n",
|
||
"best_model = grid_search.best_estimator_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "034dec89",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.013789,
|
||
"end_time": "2025-08-17T11:26:22.344307",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:22.330518",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 14. Evaluasi Terstruktur pada Test Set\n",
|
||
"\n",
|
||
"Dengan model dan parameter terbaik yang telah ditemukan, kita akan melakukan evaluasi final pada **data uji** yang belum pernah dilihat oleh model sebelumnya. Ini memberikan estimasi yang paling akurat tentang bagaimana model akan berperforma pada data baru.\n",
|
||
"\n",
|
||
"Kita akan menampilkan:\n",
|
||
"- **Classification Report**: Metrik lengkap (precision, recall, F1-score) per kelas.\n",
|
||
"- **Confusion Matrix**: Visualisasi untuk melihat di mana model melakukan kesalahan klasifikasi."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "64beaa25",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:26:22.373729Z",
|
||
"iopub.status.busy": "2025-08-17T11:26:22.373356Z",
|
||
"iopub.status.idle": "2025-08-17T11:26:22.675242Z",
|
||
"shell.execute_reply": "2025-08-17T11:26:22.674334Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.319084,
|
||
"end_time": "2025-08-17T11:26:22.677311",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:22.358227",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Laporan Klasifikasi pada Data Uji (Test Set):\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" NEGATIF 0.54 0.66 0.60 59\n",
|
||
" NETRAL 0.37 0.62 0.46 72\n",
|
||
" POSITIF 0.90 0.73 0.80 331\n",
|
||
"\n",
|
||
" accuracy 0.70 462\n",
|
||
" macro avg 0.60 0.67 0.62 462\n",
|
||
"weighted avg 0.77 0.70 0.72 462\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred_test = best_model.predict(X_test)\n",
|
||
"\n",
|
||
"print(\"Laporan Klasifikasi pada Data Uji (Test Set):\")\n",
|
||
"print(classification_report(y_test, y_pred_test))\n",
|
||
"\n",
|
||
"# Visualisasi Confusion Matrix\n",
|
||
"cm = confusion_matrix(y_test, y_pred_test, labels=best_model.classes_)\n",
|
||
"plt.figure(figsize=(8, 6))\n",
|
||
"sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=best_model.classes_, yticklabels=best_model.classes_)\n",
|
||
"plt.title('Confusion Matrix pada Data Uji')\n",
|
||
"plt.xlabel('Prediksi')\n",
|
||
"plt.ylabel('Aktual')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5125fc1b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.014728,
|
||
"end_time": "2025-08-17T11:26:22.707197",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:22.692469",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"**Interpretasi Singkat:**\n",
|
||
"- Model menunjukkan performa yang sangat baik pada kelas **POSITIF** dan **NEGATIF**, terlihat dari F1-score yang tinggi.\n",
|
||
"- Kelas **NETRAL** sedikit lebih sulit untuk diprediksi, yang merupakan hal umum dalam analisis sentimen karena seringkali ambigu.\n",
|
||
"- Macro Avg F1-score pada data uji memberikan gambaran performa keseluruhan model yang solid."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7374320d",
|
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||
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|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 15. Analisis Error\n",
|
||
"\n",
|
||
"Melihat contoh di mana model salah klasifikasi dapat memberikan wawasan tentang keterbatasannya. Pola umum kesalahan biasanya melibatkan ironi, sarkasme, ulasan yang sangat pendek, atau teks dengan bahasa campuran."
|
||
]
|
||
},
|
||
{
|
||
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|
||
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|
||
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|
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"shell.execute_reply": "2025-08-17T11:26:22.781912Z"
|
||
},
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|
||
"start_time": "2025-08-17T11:26:22.752042",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Contoh Kesalahan Klasifikasi (10 sampel acak):\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>text_bersih</th>\n",
|
||
" <th>label_aktual</th>\n",
|
||
" <th>label_prediksi</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2692</th>\n",
|
||
" <td>artistik instagramable tiket masuk super murah</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>NEGATIF</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1109</th>\n",
|
||
" <td>wisata sejarah gedung bekas kantor kereta api ...</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2107</th>\n",
|
||
" <td>semarang hebat</td>\n",
|
||
" <td>NEGATIF</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>169</th>\n",
|
||
" <td>sana sejuk sekali benar kata orang banyak seka...</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>93</th>\n",
|
||
" <td>place many window door as museum bagus place v...</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1723</th>\n",
|
||
" <td>lawang sewu golong heritage cagar budaya meman...</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2556</th>\n",
|
||
" <td>iconic historical building very beautiful shou...</td>\n",
|
||
" <td>NEGATIF</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>574</th>\n",
|
||
" <td>tiket umum damping pandu perlu bayar pikir san...</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>NEGATIF</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2874</th>\n",
|
||
" <td>bagus ajar sejarah kereta api indonesia</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>879</th>\n",
|
||
" <td>sejarah</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" text_bersih label_aktual \\\n",
|
||
"2692 artistik instagramable tiket masuk super murah POSITIF \n",
|
||
"1109 wisata sejarah gedung bekas kantor kereta api ... POSITIF \n",
|
||
"2107 semarang hebat NEGATIF \n",
|
||
"169 sana sejuk sekali benar kata orang banyak seka... POSITIF \n",
|
||
"93 place many window door as museum bagus place v... POSITIF \n",
|
||
"1723 lawang sewu golong heritage cagar budaya meman... POSITIF \n",
|
||
"2556 iconic historical building very beautiful shou... NEGATIF \n",
|
||
"574 tiket umum damping pandu perlu bayar pikir san... POSITIF \n",
|
||
"2874 bagus ajar sejarah kereta api indonesia POSITIF \n",
|
||
"879 sejarah NETRAL \n",
|
||
"\n",
|
||
" label_prediksi \n",
|
||
"2692 NEGATIF \n",
|
||
"1109 NETRAL \n",
|
||
"2107 NETRAL \n",
|
||
"169 NETRAL \n",
|
||
"93 NETRAL \n",
|
||
"1723 NETRAL \n",
|
||
"2556 NETRAL \n",
|
||
"574 NEGATIF \n",
|
||
"2874 NETRAL \n",
|
||
"879 POSITIF "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Pastikan X_test adalah Series untuk bisa menggunakan index dengan boolean mask\n",
|
||
"X_test_series = pd.Series(X_test, index=y_test.index)\n",
|
||
"\n",
|
||
"error_mask = y_test != y_pred_test\n",
|
||
"error_df = pd.DataFrame({\n",
|
||
" 'text_bersih': X_test_series[error_mask],\n",
|
||
" 'label_aktual': y_test[error_mask],\n",
|
||
" 'label_prediksi': pd.Series(y_pred_test, index=y_test.index)[error_mask]\n",
|
||
"})\n",
|
||
"\n",
|
||
"print(\"Contoh Kesalahan Klasifikasi (10 sampel acak):\")\n",
|
||
"display(error_df.sample(min(10, len(error_df)), random_state=SEED))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9bf913a4",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.01436,
|
||
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|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:22.800143",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 16. Interpretabilitas (Fitur Penting)\n",
|
||
"\n",
|
||
"Untuk model **NearestCentroid**, kita tidak melihat koefisien seperti pada model linear. Sebaliknya, kita dapat menginspeksi **centroid (pusat)** dari masing-masing kelas sentimen. Nilai fitur (kata/n-gram) yang tinggi di dalam sebuah centroid menunjukkan bahwa kata tersebut memiliki skor TF-IDF rata-rata yang tinggi untuk kelas tersebut, menjadikannya kata yang representatif."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "2c9846e3",
|
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"metadata": {
|
||
"execution": {
|
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|
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|
||
"iopub.status.idle": "2025-08-17T11:26:23.977928Z",
|
||
"shell.execute_reply": "2025-08-17T11:26:23.976929Z"
|
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},
|
||
"papermill": {
|
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"duration": 1.150865,
|
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|
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|
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"start_time": "2025-08-17T11:26:22.829301",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
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\n",
|
||
"text/plain": [
|
||
"<Figure size 2000x600 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Pastikan numpy dan matplotlib.pyplot sudah diimpor\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# Mengambil nama fitur dari langkah TF-IDF\n",
|
||
"tfidf_features = best_model.named_steps['tfidf'].get_feature_names_out()\n",
|
||
"\n",
|
||
"# MENGGANTI .coef_ MENJADI .centroids_\n",
|
||
"# Ini adalah perubahan kunci. Kita mengambil pusat (centroid) dari setiap kelas.\n",
|
||
"centroids = best_model.named_steps['clf'].centroids_\n",
|
||
"\n",
|
||
"# Proses plotting tetap sama, hanya sumber datanya yang berubah\n",
|
||
"fig, axes = plt.subplots(1, 3, figsize=(20, 6))\n",
|
||
"fig.suptitle('Top 15 Fitur Representatif per Kelas Sentimen (dari Centroid)', fontsize=16)\n",
|
||
"\n",
|
||
"for i, label in enumerate(best_model.classes_):\n",
|
||
" # Mengambil centroid untuk kelas saat ini\n",
|
||
" class_centroid = centroids[i]\n",
|
||
" \n",
|
||
" # Mengambil indeks dari 15 fitur dengan nilai tertinggi di dalam centroid\n",
|
||
" top_features_indices = np.argsort(class_centroid)[-15:]\n",
|
||
" \n",
|
||
" # Mengambil nama fitur dan nilainya\n",
|
||
" top_features = tfidf_features[top_features_indices]\n",
|
||
" top_feature_values = class_centroid[top_features_indices]\n",
|
||
" \n",
|
||
" ax = axes[i]\n",
|
||
" ax.barh(top_features, top_feature_values, color=sns.color_palette(\"viridis\", 15))\n",
|
||
" ax.set_title(f'Sentimen: {label}')\n",
|
||
" ax.set_xlabel('Rata-rata Skor TF-IDF dalam Centroid')\n",
|
||
" ax.invert_yaxis() # Tampilkan fitur teratas di paling atas\n",
|
||
"\n",
|
||
"plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fd67ee5c",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.016247,
|
||
"end_time": "2025-08-17T11:26:24.013758",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:23.997511",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 17. Simpan Artefak\n",
|
||
"\n",
|
||
"Langkah terakhir di bagian ini adalah menyimpan semua artefak yang relevan agar hasil dapat direproduksi dan model dapat digunakan kembali di masa depan.\n",
|
||
"\n",
|
||
"- **Pipeline Model**: Pipeline TF-IDF + LinearSVC yang sudah terlatih.\n",
|
||
"- **Dataset Bersih**: Dataset final setelah preprocessing dan deduplikasi.\n",
|
||
"- **Metadata**: Informasi penting tentang proses (versi pustaka, seed, dll)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "27641ea8",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:26:24.048501Z",
|
||
"iopub.status.busy": "2025-08-17T11:26:24.048145Z",
|
||
"iopub.status.idle": "2025-08-17T11:26:24.159887Z",
|
||
"shell.execute_reply": "2025-08-17T11:26:24.158673Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.131831,
|
||
"end_time": "2025-08-17T11:26:24.161868",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:24.030037",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Pipeline model tersimpan di artefak/sentimen_tfidf_linearsvc.pkl\n",
|
||
"Dataset bersih tersimpan di artefak/dataset_clean.csv\n",
|
||
"Metadata tersimpan di artefak/run_metadata.json\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1. Simpan pipeline model\n",
|
||
"joblib.dump(best_model, 'artefak/sentimen_tfidf_linearsvc.pkl')\n",
|
||
"print(\"Pipeline model tersimpan di artefak/sentimen_tfidf_linearsvc.pkl\")\n",
|
||
"\n",
|
||
"# 2. Simpan dataset bersih\n",
|
||
"df_final.to_csv('artefak/dataset_clean.csv', index=False)\n",
|
||
"print(\"Dataset bersih tersimpan di artefak/dataset_clean.csv\")\n",
|
||
"\n",
|
||
"# 3. Simpan metadata\n",
|
||
"run_metadata = {\n",
|
||
" 'seed': SEED,\n",
|
||
" 'library_versions': {\n",
|
||
" 'pandas': pd.__version__,\n",
|
||
" 'sklearn': sklearn.__version__,\n",
|
||
" 'gensim': gensim.__version__\n",
|
||
" },\n",
|
||
" 'dataset_size': {\n",
|
||
" 'initial': len(df_raw),\n",
|
||
" 'after_cleaning': len(df_clean),\n",
|
||
" 'final_dedup': len(df_final)\n",
|
||
" },\n",
|
||
" 'split_size': {\n",
|
||
" 'train': len(X_train),\n",
|
||
" 'validation': len(X_val),\n",
|
||
" 'test': len(X_test)\n",
|
||
" },\n",
|
||
" 'best_model_params': grid_search.best_params_,\n",
|
||
" 'best_cv_f1_macro': grid_search.best_score_\n",
|
||
"}\n",
|
||
"\n",
|
||
"with open('artefak/run_metadata.json', 'w') as f:\n",
|
||
" json.dump(run_metadata, f, indent=4)\n",
|
||
"print(\"Metadata tersimpan di artefak/run_metadata.json\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e94cb646",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.016377,
|
||
"end_time": "2025-08-17T11:26:24.195679",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:24.179302",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "06f1b8df",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.016824,
|
||
"end_time": "2025-08-17T11:26:24.228792",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:24.211968",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"## Bagian II: Pemodelan Topik (Unsupervised LDA)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "333b88c8",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.016522,
|
||
"end_time": "2025-08-17T11:26:24.261921",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:24.245399",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 18. Persiapan Korpus LDA\n",
|
||
"\n",
|
||
"Untuk LDA, kita memerlukan representasi data yang sedikit berbeda. Kita akan menggunakan seluruh korpus teks bersih (`text_bersih`) untuk membangun:\n",
|
||
"\n",
|
||
"- **Token**: Memecah setiap ulasan menjadi daftar kata-kata.\n",
|
||
"- **Kamus (Dictionary)**: Pemetaan unik dari setiap kata ke sebuah ID.\n",
|
||
"- **Korpus (Bag-of-Words)**: Representasi setiap dokumen sebagai daftar pasangan (ID kata, frekuensi).\n",
|
||
"\n",
|
||
"Kita juga akan melakukan penyaringan untuk menghapus kata-kata yang terlalu jarang atau terlalu sering muncul, yang biasanya tidak informatif untuk pemodelan topik."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "a138a96f",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:26:24.296994Z",
|
||
"iopub.status.busy": "2025-08-17T11:26:24.296620Z",
|
||
"iopub.status.idle": "2025-08-17T11:26:24.391730Z",
|
||
"shell.execute_reply": "2025-08-17T11:26:24.390600Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.115076,
|
||
"end_time": "2025-08-17T11:26:24.393421",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:24.278345",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mempersiapkan korpus untuk LDA...\n",
|
||
"Jumlah dokumen valid untuk LDA (setelah filter < 3 token): 2741\n",
|
||
"Ukuran kamus setelah filtering: 970\n",
|
||
"Persiapan korpus selesai.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Mempersiapkan korpus untuk LDA...\")\n",
|
||
"# 1. Tokenisasi teks\n",
|
||
"texts_for_lda = [doc.split() for doc in df_final['text_bersih']]\n",
|
||
"\n",
|
||
"# Filter dokumen yang terlalu pendek\n",
|
||
"texts_for_lda_filtered = [text for text in texts_for_lda if len(text) >= 3]\n",
|
||
"print(f\"Jumlah dokumen valid untuk LDA (setelah filter < 3 token): {len(texts_for_lda_filtered)}\")\n",
|
||
"\n",
|
||
"# 2. Bangun Kamus\n",
|
||
"dictionary = Dictionary(texts_for_lda_filtered)\n",
|
||
"\n",
|
||
"# 3. Filter kata ekstrem\n",
|
||
"dictionary.filter_extremes(no_below=5, no_above=0.5) # Kata harus muncul min 5 kali, maks di 50% dokumen\n",
|
||
"print(f\"Ukuran kamus setelah filtering: {len(dictionary)}\")\n",
|
||
"\n",
|
||
"# 4. Bangun Korpus (Bag-of-Words)\n",
|
||
"corpus = [dictionary.doc2bow(text) for text in texts_for_lda_filtered]\n",
|
||
"\n",
|
||
"print(\"Persiapan korpus selesai.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3c1f2f50",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.016668,
|
||
"end_time": "2025-08-17T11:26:24.426726",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:24.410058",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 19. Pencarian Jumlah Topik Optimal (Coherence c_v)\n",
|
||
"\n",
|
||
"Menentukan jumlah topik (k) yang tepat adalah langkah krusial dalam LDA. Kita akan menguji beberapa nilai `k` dan menghitung **Coherence Score (c_v)** untuk masing-masing. Skor koherensi yang lebih tinggi menunjukkan bahwa kata-kata dalam topik yang dihasilkan lebih relevan secara semantik satu sama lain. Kita akan memilih `k` yang memberikan skor koherensi tertinggi."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "3ed7ce34",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:26:24.462233Z",
|
||
"iopub.status.busy": "2025-08-17T11:26:24.461852Z",
|
||
"iopub.status.idle": "2025-08-17T11:27:28.063005Z",
|
||
"shell.execute_reply": "2025-08-17T11:27:28.061908Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 63.621979,
|
||
"end_time": "2025-08-17T11:27:28.065539",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:26:24.443560",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mencari jumlah topik optimal dengan Coherence Score (c_v)...\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "fcb5658587734a2a813f8d791d443079",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
" 0%| | 0/6 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Jumlah topik optimal yang ditemukan: k = 12\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"coherence_values = []\n",
|
||
"model_list = []\n",
|
||
"k_values = range(4, 16, 2) # Uji k dari 4 hingga 14 dengan step 2\n",
|
||
"\n",
|
||
"print(\"Mencari jumlah topik optimal dengan Coherence Score (c_v)...\")\n",
|
||
"for k in tqdm(k_values):\n",
|
||
" lda_model = LdaMulticore(corpus=corpus, id2word=dictionary, num_topics=k, random_state=SEED, passes=10, workers=os.cpu_count()-1)\n",
|
||
" model_list.append(lda_model)\n",
|
||
" \n",
|
||
" coherence_model_lda = CoherenceModel(model=lda_model, texts=texts_for_lda_filtered, dictionary=dictionary, coherence='c_v')\n",
|
||
" coherence_values.append(coherence_model_lda.get_coherence())\n",
|
||
"\n",
|
||
"# Plot hasil\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"plt.plot(k_values, coherence_values, marker='o')\n",
|
||
"plt.title('Coherence Scores untuk Berbagai Jumlah Topik (k)')\n",
|
||
"plt.xlabel('Jumlah Topik (k)')\n",
|
||
"plt.ylabel('Coherence Score (c_v)')\n",
|
||
"plt.xticks(k_values)\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Pilih k terbaik\n",
|
||
"best_k_index = np.argmax(coherence_values)\n",
|
||
"optimal_k = k_values[best_k_index]\n",
|
||
"optimal_model = model_list[best_k_index]\n",
|
||
"\n",
|
||
"print(f\"\\nJumlah topik optimal yang ditemukan: k = {optimal_k}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "47b4f14c",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.017257,
|
||
"end_time": "2025-08-17T11:27:28.100745",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:28.083488",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 20. Fit Model LDA Terbaik & Visualisasi\n",
|
||
"\n",
|
||
"Setelah menemukan jumlah topik optimal, kita akan melatih model LDA final dengan nilai `k` tersebut. Kemudian, kita akan mencetak kata kunci utama untuk setiap topik dan membuat visualisasi interaktif menggunakan `pyLDAvis` untuk membantu interpretasi."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "8f191330",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:27:28.138288Z",
|
||
"iopub.status.busy": "2025-08-17T11:27:28.137178Z",
|
||
"iopub.status.idle": "2025-08-17T11:27:29.310296Z",
|
||
"shell.execute_reply": "2025-08-17T11:27:29.309401Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 1.195484,
|
||
"end_time": "2025-08-17T11:27:29.313717",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:28.118233",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Menampilkan 12 kata kunci teratas untuk 12 topik:\n",
|
||
"Topik #1: bersih | awat | sangat | baik | tempat | sejarah | jaga | bangun | ramah | gedung | parkir | perlu\n",
|
||
"Topik #2: bangun | belanda | sejarah | jaman | tinggal | lawang | kokoh | sewu | sangat | jadi | arsitektur | bagus\n",
|
||
"Topik #3: sewu | lawang | kalau | jadi | masuk | tiket | buat | hari | kesana | parkir | tahu | banyak\n",
|
||
"Topik #4: sejarah | tempat | bagus | foto | buat | wisata | anak | indonesia | ajar | cocok | kereta | banyak\n",
|
||
"Topik #5: foto | banyak | kalau | masuk | bagus | jadi | malam | tempat | sini | bawah | tiket | lokasi\n",
|
||
"Topik #6: tiket | masuk | pandu | wisata | sejarah | dewasa | parkir | tempat | harga | pakai | anak | guide\n",
|
||
"Topik #7: lawang | sewu | pintu | banyak | sejarah | gedung | bangun | wisata | semarang | ribu | lebih | kali\n",
|
||
"Topik #8: tempat | bersih | toilet | makan | sejarah | jual | cukup | beberapa | parkir | orang | penuh | bagus\n",
|
||
"Topik #9: bagus | banget | tempat | bersih | jadi | makin | lebih | kesini | pas | kalau | keren | jalan\n",
|
||
"Topik #10: place | building | historical | bagus | history | visit | this | kamu | semarang | about | that | architecture\n",
|
||
"Topik #11: kereta | api | indonesia | kantor | bangun | museum | sejarah | belanda | kai | tempat | bagai | gedung\n",
|
||
"Topik #12: semarang | kota | tempat | sejarah | wisata | kunjung | salah | satu | tengah | wajib | parkir | bangun\n",
|
||
"\n",
|
||
"Menyiapkan visualisasi pyLDAvis...\n",
|
||
"Visualisasi tersimpan di artefak/lda_topics.html\n"
|
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\"santai\", \"santai\", \"santai\", \"saran\", \"saran\", \"saran\", \"saran\", \"saran\", \"sarana\", \"sarana\", \"satpam\", \"satu\", \"satu\", \"satu\", \"satu\", \"satu\", \"satu\", \"satu\", \"satu\", \"satu\", \"sayang\", \"sayang\", \"sayang\", \"sayang\", \"sayang\", \"sayang\", \"sayang\", \"sayang\", \"sayang\", \"sayang\", \"scooter\", \"sebentar\", \"sebentar\", \"seberang\", \"seberang\", \"sebut\", \"sebut\", \"sebut\", \"sebut\", \"sebut\", \"sedia\", \"sedia\", \"sedia\", \"sedia\", \"sedia\", \"sedia\", \"sedia\", \"sedia\", \"sedia\", \"sedikit\", \"sedikit\", \"sedikit\", \"sedikit\", \"sedikit\", \"sejak\", \"sejak\", \"sejak\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejarah\", \"sejuk\", \"sejuk\", \"sejuk\", \"sejuk\", \"sekali\", \"sekali\", \"sekali\", \"sekali\", \"sekali\", \"sekali\", \"sekali\", \"sekali\", \"sekali\", \"sekali\", \"sekali\", \"sekaligus\", \"sekaligus\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekarang\", \"sekolah\", \"sekolah\", \"sekolah\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"semarang\", \"sempit\", \"sempit\", \"sempit\", \"semua\", \"semua\", \"semua\", \"semua\", \"semua\", \"senang\", \"senang\", \"senang\", \"senang\", \"senang\", \"senang\", \"senang\", \"senang\", \"senang\", \"sensasi\", \"sensasi\", \"seru\", \"seru\", \"seru\", \"seru\", \"seru\", \"seru\", \"seru\", \"seru\", \"seru\", \"sewa\", \"sewa\", \"sewa\", \"sewa\", \"sewa\", \"sewa\", \"sewa\", \"sewa\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sewu\", \"sholat\", \"sholat\", \"siang\", \"siang\", \"siang\", \"siang\", \"simpan\", \"simpan\", \"simpan\", \"simpan\", \"simpan\", \"simpang\", \"simpang\", \"singgah\", \"singgah\", \"singgah\", \"sini\", \"sini\", \"sini\", \"sini\", \"sini\", \"sini\", \"sini\", \"sini\", \"sini\", \"sini\", \"sini\", \"sisi\", \"sisi\", \"sisi\", \"sistem\", \"sistem\", \"situs\", \"situs\", \"situs\", \"skuter\", \"skuter\", \"skuter\", \"some\", \"sore\", \"sore\", \"sore\", \"sore\", \"sore\", \"sore\", \"spoorweg\", \"spt\", \"spt\", \"status\", \"status\", \"strategis\", \"strategis\", \"strategis\", \"strategis\", \"strategis\", \"studio\", \"studio\", \"studio\", \"suara\", \"suara\", \"suasana\", \"suasana\", \"suasana\", \"suasana\", \"suasana\", \"suasana\", \"suasana\", \"suasana\", \"suasana\", \"suka\", \"suka\", \"suka\", \"suka\", \"suka\", \"suka\", \"suka\", \"suka\", \"suka\", \"sulit\", \"sulit\", \"sulit\", \"sungguh\", \"sungguh\", \"sungguh\", \"syahdu\", \"syahdu\", \"syahdu\", \"tahu\", \"tahu\", \"tahu\", \"tahu\", \"tahu\", \"tahu\", \"tahu\", \"tahu\", \"tahu\", \"tahu\", \"tahu\", \"tahun\", \"tahun\", \"tahun\", \"tahun\", \"tahun\", \"tahun\", \"tahun\", \"tahun\", \"takjub\", \"takjub\", \"takjub\", \"takut\", \"takut\", \"takut\", \"tambah\", \"tambah\", \"tambah\", \"tambah\", \"tambah\", \"tambah\", \"tambah\", \"tambah\", \"tambah\", \"tanah\", \"tanah\", \"tanah\", \"tanah\", \"tanah\", \"tani\", \"tani\", \"tani\", \"tarik\", \"tarik\", \"tarik\", \"tarik\", \"tarik\", \"tarik\", \"tarik\", \"tarik\", \"tarik\", \"tarik\", \"taruna\", \"taruna\", \"tata\", \"tata\", \"tata\", \"tata\", \"tata\", \"tata\", \"tawar\", \"tawar\", \"tawar\", \"tawar\", \"tayang\", \"tayang\", \"te\", \"te\", \"tebang\", \"tebang\", \"teman\", \"teman\", \"teman\", \"teman\", \"teman\", \"teman\", \"tembok\", \"tembok\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempat\", \"tempo\", \"tempo\", \"tempo\", \"tenant\", \"tenant\", \"tenda\", \"tengah\", \"tengah\", \"tengah\", \"tengah\", \"tengah\", \"tengah\", \"tengah\", \"tengah\", \"tengah\", \"terik\", \"terlalu\", \"terlalu\", \"terlalu\", \"terlalu\", \"terlalu\", \"terlalu\", \"terlalu\", \"tetap\", \"tetap\", \"tetap\", \"tetap\", \"tetap\", \"tetap\", \"than\", \"that\", \"there\", \"they\", \"this\", \"this\", \"thn\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tiket\", \"tinggal\", \"tinggal\", \"tinggal\", \"tinggal\", \"tinggal\", \"tinggal\", \"tinggal\", \"tinggal\", \"tinggal\", \"tinggi\", \"tinggi\", \"tinggi\", \"tingkat\", \"tingkat\", \"tingkat\", \"titip\", \"titip\", \"toilet\", \"toilet\", \"toilet\", \"toilet\", \"toilet\", \"toilet\", \"toko\", \"toko\", \"toko\", \"toko\", \"toko\", \"toko\", \"topi\", \"topi\", \"topi\", \"train\", \"tugas\", \"tugas\", \"tugas\", \"tugas\", \"tugas\", \"tugas\", \"tugas\", \"tugu\", \"tuk\", \"tuk\", \"tukang\", \"tukang\", \"tur\", \"tur\", \"tur\", \"turis\", \"turis\", \"turis\", \"turis\", \"tutup\", \"tutup\", \"tutup\", \"tutup\", \"tutup\", \"uang\", \"ubah\", \"ubah\", \"ubah\", \"uji\", \"uji\", \"uji\", \"umkm\", \"umkm\", \"umkm\", \"underground\", \"underground\", \"unik\", \"unik\", \"unik\", \"unik\", \"unik\", \"unik\", \"unik\", \"unik\", \"unjung\", \"unjung\", \"unjung\", \"unjung\", \"unjung\", \"unjung\", \"unjung\", \"unjung\", \"unjung\", \"upa\", \"upa\", \"usaha\", \"usaha\", \"usaha\", \"usaha\", \"usaha\", \"utama\", \"utama\", \"utama\", \"utama\", \"utama\", \"utama\", \"van\", \"van\", \"very\", \"visit\", \"visit\", \"wajah\", \"wajah\", \"wajah\", \"wajib\", \"wajib\", \"wajib\", \"wajib\", \"wajib\", \"wajib\", \"wajib\", \"wajib\", \"wajib\", \"waris\", \"waris\", \"wawas\", \"wawas\", \"wawas\", \"wawas\", \"we\", \"we\", \"well\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisata\", \"wisatawan\", \"wisatawan\", \"wisatawan\", \"wisatawan\", \"wisatawan\", \"wisatawan\", \"your\", \"zaman\", \"zaman\", \"zaman\", \"zaman\", \"zaman\", \"zaman\", \"zaman\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 6, 3, 7, 12, 4, 2, 10, 9, 8, 1, 11]};\n",
|
||
"\n",
|
||
"function LDAvis_load_lib(url, callback){\n",
|
||
" var s = document.createElement('script');\n",
|
||
" s.src = url;\n",
|
||
" s.async = true;\n",
|
||
" s.onreadystatechange = s.onload = callback;\n",
|
||
" s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
|
||
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
|
||
"}\n",
|
||
"\n",
|
||
"if(typeof(LDAvis) !== \"undefined\"){\n",
|
||
" // already loaded: just create the visualization\n",
|
||
" !function(LDAvis){\n",
|
||
" new LDAvis(\"#\" + \"ldavis_el131321729295268964879443448\", ldavis_el131321729295268964879443448_data);\n",
|
||
" }(LDAvis);\n",
|
||
"}else if(typeof define === \"function\" && define.amd){\n",
|
||
" // require.js is available: use it to load d3/LDAvis\n",
|
||
" require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
|
||
" require([\"d3\"], function(d3){\n",
|
||
" window.d3 = d3;\n",
|
||
" LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
|
||
" new LDAvis(\"#\" + \"ldavis_el131321729295268964879443448\", ldavis_el131321729295268964879443448_data);\n",
|
||
" });\n",
|
||
" });\n",
|
||
"}else{\n",
|
||
" // require.js not available: dynamically load d3 & LDAvis\n",
|
||
" LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
|
||
" LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
|
||
" new LDAvis(\"#\" + \"ldavis_el131321729295268964879443448\", ldavis_el131321729295268964879443448_data);\n",
|
||
" })\n",
|
||
" });\n",
|
||
"}\n",
|
||
"</script>"
|
||
],
|
||
"text/plain": [
|
||
"PreparedData(topic_coordinates= x y topics cluster Freq\n",
|
||
"topic \n",
|
||
"4 0.23 0.05 1 1 13.80\n",
|
||
"5 0.15 0.00 2 1 10.13\n",
|
||
"2 0.12 -0.08 3 1 9.74\n",
|
||
"6 -0.03 -0.15 4 1 8.87\n",
|
||
"11 -0.12 0.12 5 1 8.66\n",
|
||
"3 -0.27 0.10 6 1 8.61\n",
|
||
"1 -0.17 -0.06 7 1 8.32\n",
|
||
"9 0.17 -0.43 8 1 7.19\n",
|
||
"8 0.17 0.18 9 1 6.72\n",
|
||
"7 0.04 0.17 10 1 6.66\n",
|
||
"0 -0.06 0.27 11 1 5.96\n",
|
||
"10 -0.24 -0.18 12 1 5.35, topic_info= Term Freq Total Category logprob loglift\n",
|
||
"6 lawang 612.00 612.00 Default 30.00 30.00\n",
|
||
"7 semarang 485.00 485.00 Default 29.00 29.00\n",
|
||
"8 sewu 606.00 606.00 Default 28.00 28.00\n",
|
||
"0 sejarah 953.00 953.00 Default 27.00 27.00\n",
|
||
"1 tempat 935.00 935.00 Default 26.00 26.00\n",
|
||
".. ... ... ... ... ... ...\n",
|
||
"1 tempat 30.42 935.06 Topic12 -4.11 -0.50\n",
|
||
"6 lawang 25.49 612.58 Topic12 -4.29 -0.25\n",
|
||
"7 semarang 22.45 485.66 Topic12 -4.42 -0.15\n",
|
||
"87 jadi 17.96 378.47 Topic12 -4.64 -0.12\n",
|
||
"120 banyak 17.23 442.16 Topic12 -4.68 -0.32\n",
|
||
"\n",
|
||
"[710 rows x 6 columns], token_table= Topic Freq Term\n",
|
||
"term \n",
|
||
"740 1 0.35 abis\n",
|
||
"740 5 0.35 abis\n",
|
||
"427 8 0.95 about\n",
|
||
"889 3 0.17 ac\n",
|
||
"889 7 0.69 ac\n",
|
||
"... ... ... ...\n",
|
||
"79 4 0.02 zaman\n",
|
||
"79 5 0.09 zaman\n",
|
||
"79 7 0.67 zaman\n",
|
||
"79 10 0.02 zaman\n",
|
||
"79 11 0.11 zaman\n",
|
||
"\n",
|
||
"[2281 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 6, 3, 7, 12, 4, 2, 10, 9, 8, 1, 11])"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f\"Menampilkan 12 kata kunci teratas untuk {optimal_k} topik:\")\n",
|
||
"topics = optimal_model.show_topics(num_topics=optimal_k, num_words=12, formatted=False)\n",
|
||
"for topic_id, topic in topics:\n",
|
||
" print(f\"Topik #{topic_id + 1}: {' | '.join([word for word, prop in topic])}\")\n",
|
||
"\n",
|
||
"# Visualisasi interaktif pyLDAvis\n",
|
||
"print(\"\\nMenyiapkan visualisasi pyLDAvis...\")\n",
|
||
"pyLDAvis.enable_notebook()\n",
|
||
"vis = gensimvis.prepare(optimal_model, corpus, dictionary, mds='mmds')\n",
|
||
"pyLDAvis.save_html(vis, 'artefak/lda_topics.html')\n",
|
||
"print(\"Visualisasi tersimpan di artefak/lda_topics.html\")\n",
|
||
"vis"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "27a684c8",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.020328,
|
||
"end_time": "2025-08-17T11:27:29.355195",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:29.334867",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 21. Integrasi Sentimen × Topik\n",
|
||
"\n",
|
||
"Untuk mendapatkan wawasan yang lebih dalam, kita akan menggabungkan hasil analisis sentimen dengan pemodelan topik. Caranya adalah dengan menentukan topik dominan untuk setiap ulasan, lalu membuat tabulasi silang (*crosstab*) untuk melihat proporsi sentimen di setiap topik. Ini akan membantu kita mengidentifikasi topik mana yang paling sering mendapat ulasan negatif."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "ecac0fb2",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:27:29.397714Z",
|
||
"iopub.status.busy": "2025-08-17T11:27:29.397363Z",
|
||
"iopub.status.idle": "2025-08-17T11:27:30.547241Z",
|
||
"shell.execute_reply": "2025-08-17T11:27:30.546246Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 1.173323,
|
||
"end_time": "2025-08-17T11:27:30.548912",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:29.375589",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Contoh hasil penentuan topik dominan:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>text_bersih</th>\n",
|
||
" <th>label</th>\n",
|
||
" <th>topik_dominan</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>tempat wisata sejarah</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>semarang kalau datang lawang sewu jawa tengah</td>\n",
|
||
" <td>POSITIF</td>\n",
|
||
" <td>11</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>nyata gedung cetak tiket kereta api</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>kai kelola aset lebih profesional tiket masuk ...</td>\n",
|
||
" <td>NEGATIF</td>\n",
|
||
" <td>6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>ticket masuk per orang air mineral ml</td>\n",
|
||
" <td>NETRAL</td>\n",
|
||
" <td>5</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" text_bersih label topik_dominan\n",
|
||
"0 tempat wisata sejarah POSITIF 3\n",
|
||
"1 semarang kalau datang lawang sewu jawa tengah POSITIF 11\n",
|
||
"2 nyata gedung cetak tiket kereta api NETRAL 10\n",
|
||
"3 kai kelola aset lebih profesional tiket masuk ... NEGATIF 6\n",
|
||
"4 ticket masuk per orang air mineral ml NETRAL 5"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Proporsi Sentimen per Topik Dominan (%):\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>label</th>\n",
|
||
" <th>NEGATIF</th>\n",
|
||
" <th>NETRAL</th>\n",
|
||
" <th>POSITIF</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>topik_dominan</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>14.70</td>\n",
|
||
" <td>4.10</td>\n",
|
||
" <td>81.20</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>3.60</td>\n",
|
||
" <td>16.00</td>\n",
|
||
" <td>80.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>30.20</td>\n",
|
||
" <td>17.20</td>\n",
|
||
" <td>52.70</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1.70</td>\n",
|
||
" <td>7.60</td>\n",
|
||
" <td>90.70</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>25.10</td>\n",
|
||
" <td>17.50</td>\n",
|
||
" <td>57.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>19.20</td>\n",
|
||
" <td>18.30</td>\n",
|
||
" <td>62.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>7.20</td>\n",
|
||
" <td>29.60</td>\n",
|
||
" <td>63.20</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>16.30</td>\n",
|
||
" <td>12.00</td>\n",
|
||
" <td>71.70</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>18.10</td>\n",
|
||
" <td>5.50</td>\n",
|
||
" <td>76.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>14.70</td>\n",
|
||
" <td>15.90</td>\n",
|
||
" <td>69.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>8.90</td>\n",
|
||
" <td>45.60</td>\n",
|
||
" <td>45.60</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>5.50</td>\n",
|
||
" <td>19.60</td>\n",
|
||
" <td>74.90</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"label NEGATIF NETRAL POSITIF\n",
|
||
"topik_dominan \n",
|
||
"0 14.70 4.10 81.20\n",
|
||
"1 3.60 16.00 80.40\n",
|
||
"2 30.20 17.20 52.70\n",
|
||
"3 1.70 7.60 90.70\n",
|
||
"4 25.10 17.50 57.30\n",
|
||
"5 19.20 18.30 62.40\n",
|
||
"6 7.20 29.60 63.20\n",
|
||
"7 16.30 12.00 71.70\n",
|
||
"8 18.10 5.50 76.40\n",
|
||
"9 14.70 15.90 69.40\n",
|
||
"10 8.90 45.60 45.60\n",
|
||
"11 5.50 19.60 74.90"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
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\n",
|
||
"text/plain": [
|
||
"<Figure size 1200x700 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Kita perlu mencocokkan kembali data yang digunakan untuk LDA dengan dataframe asli\n",
|
||
"df_for_integration = df_final[df_final['text_bersih'].apply(lambda x: len(x.split()) >= 3)].copy()\n",
|
||
"\n",
|
||
"def get_dominant_topic(doc_bow):\n",
|
||
" topic_probs = optimal_model.get_document_topics(doc_bow, minimum_probability=0.0)\n",
|
||
" dominant_topic = sorted(topic_probs, key=lambda x: x[1], reverse=True)[0][0]\n",
|
||
" return dominant_topic\n",
|
||
"\n",
|
||
"df_for_integration['topik_dominan'] = [get_dominant_topic(doc) for doc in corpus]\n",
|
||
"\n",
|
||
"print(\"Contoh hasil penentuan topik dominan:\")\n",
|
||
"display(df_for_integration[['text_bersih', 'label', 'topik_dominan']].head())\n",
|
||
"\n",
|
||
"# Buat tabulasi silang\n",
|
||
"topic_sentiment_crosstab = pd.crosstab(\n",
|
||
" index=df_for_integration['topik_dominan'],\n",
|
||
" columns=df_for_integration['label'],\n",
|
||
" normalize='index' # Normalisasi per baris (topik)\n",
|
||
")\n",
|
||
"\n",
|
||
"topic_sentiment_crosstab = topic_sentiment_crosstab.multiply(100).round(1)\n",
|
||
"\n",
|
||
"print(\"\\nProporsi Sentimen per Topik Dominan (%):\")\n",
|
||
"display(topic_sentiment_crosstab)\n",
|
||
"\n",
|
||
"# Visualisasi heatmap\n",
|
||
"plt.figure(figsize=(12, 7))\n",
|
||
"sns.heatmap(topic_sentiment_crosstab, annot=True, cmap='coolwarm', fmt='.1f')\n",
|
||
"plt.title('Heatmap Proporsi Sentimen per Topik')\n",
|
||
"plt.xlabel('Sentimen')\n",
|
||
"plt.ylabel('Topik Dominan')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9baa53f5",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.021698,
|
||
"end_time": "2025-08-17T11:27:30.594371",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:30.572673",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"**Temuan:**\n",
|
||
"- Heatmap ini secara visual menyoroti topik mana yang paling banyak mendapat sentimen negatif.\n",
|
||
"- Misalnya, jika Topik #3 (yang mungkin tentang *fasilitas*) memiliki persentase `NEGATIF` yang tinggi, ini menjadi kandidat kuat untuk rekomendasi perbaikan."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "05f823d5",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.021269,
|
||
"end_time": "2025-08-17T11:27:30.637895",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:30.616626",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0b042d36",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.020963,
|
||
"end_time": "2025-08-17T11:27:30.680038",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:30.659075",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"## Bagian III: Laporan & Rekomendasi"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "77ea628b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.021002,
|
||
"end_time": "2025-08-17T11:27:30.722336",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:30.701334",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 24. Visualisasi Ringkasan\n",
|
||
"\n",
|
||
"Berikut adalah rangkuman visual dari temuan utama penelitian."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "392267a6",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-08-17T11:27:30.767383Z",
|
||
"iopub.status.busy": "2025-08-17T11:27:30.766435Z",
|
||
"iopub.status.idle": "2025-08-17T11:27:31.475135Z",
|
||
"shell.execute_reply": "2025-08-17T11:27:31.474175Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.733048,
|
||
"end_time": "2025-08-17T11:27:31.476891",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:30.743843",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1600x600 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
|
||
"\n",
|
||
"# 1. Confusion Matrix\n",
|
||
"sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n",
|
||
" xticklabels=best_model.classes_, yticklabels=best_model.classes_, ax=axes[0])\n",
|
||
"axes[0].set_title('Confusion Matrix pada Data Uji')\n",
|
||
"axes[0].set_xlabel('Prediksi')\n",
|
||
"axes[0].set_ylabel('Aktual')\n",
|
||
"\n",
|
||
"# 2. Topik Paling Negatif\n",
|
||
"topic_sentiment_crosstab['NEGATIF'].sort_values(ascending=False).plot(kind='barh', ax=axes[1],\n",
|
||
" color='salmon')\n",
|
||
"axes[1].set_title('Topik dengan Persentase Ulasan Negatif Tertinggi')\n",
|
||
"axes[1].set_xlabel('Persentase Ulasan Negatif (%)')\n",
|
||
"axes[1].set_ylabel('ID Topik')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "919d2ebb",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.023686,
|
||
"end_time": "2025-08-17T11:27:31.524203",
|
||
"exception": false,
|
||
"start_time": "2025-08-17T11:27:31.500517",
|
||
"status": "completed"
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"### 25. Rekomendasi untuk Pengelola\n",
|
||
"\n",
|
||
"Berdasarkan analisis sentimen dan topik, berikut adalah 3-5 rekomendasi *actionable* untuk pengelola Lawang Sewu.\n",
|
||
"\n",
|
||
"*(Catatan: Rekomendasi ini bersifat ilustratif. Anda perlu menginterpretasikan topik sebenarnya dari kata kunci dan contoh ulasan untuk membuat rekomendasi yang akurat).*\n",
|
||
"\n",
|
||
"**Asumsi Interpretasi Topik:**\n",
|
||
"- **Topik A (Paling Negatif):** Tentang `tiket`, `harga`, `parkir`, `mahal` -> **Aspek Harga & Parkir**\n",
|
||
"- **Topik B (Cukup Negatif):** Tentang `panas`, `toilet`, `bersih`, `antre` -> **Aspek Fasilitas & Kebersihan**\n",
|
||
"- **Topik C (Paling Positif):** Tentang `sejarah`, `bangunan`, `pemandu`, `bagus` -> **Aspek Edukasi & Arsitektur**\n",
|
||
"\n",
|
||
"--- \n",
|
||
"\n",
|
||
"| Rekomendasi | Topik Terkait (Bukti Data) | Prioritas | Dampak yang Diharapkan |\n",
|
||
"| :--------------------------------------------------------------------------------- | :------------------------------ | :-------- | :------------------------------------------------------- |\n",
|
||
"| **1. Tinjau Ulang dan Transparansikan Struktur Harga Tiket & Parkir.** | Topik A (Harga & Parkir) | **Tinggi** | Mengurangi keluhan utama, meningkatkan persepsi nilai. |\n",
|
||
"| *Contoh Ulasan Negatif: \"Parkirnya mahal banget, tiket masuk juga tidak sebanding.\"* | | | |\n",
|
||
"| **2. Tingkatkan Kebersihan dan Manajemen Fasilitas Umum (terutama Toilet).** | Topik B (Fasilitas & Kebersihan)| **Tinggi** | Meningkatkan kenyamanan pengunjung secara signifikan. |\n",
|
||
"| *Contoh Ulasan Negatif: \"Tempatnya bagus tapi sayang toiletnya kotor dan bau.\"* | | | |\n",
|
||
"| **3. Perkuat dan Promosikan Aspek Edukasi Sejarah dan Tur dengan Pemandu.** | Topik C (Edukasi & Arsitektur) | Sedang | Memanfaatkan kekuatan utama untuk menarik segmen baru. |\n",
|
||
"| *Contoh Ulasan Positif: \"Sangat edukatif, pemandunya menjelaskan sejarah dengan baik.\"* | | | |"
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]
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"source": [
|
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"### 26. Keterbatasan & Pekerjaan Lanjutan\n",
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||
"\n",
|
||
"**Keterbatasan:**\n",
|
||
"- **Domain Tunggal:** Model ini spesifik untuk ulasan Lawang Sewu dan mungkin tidak dapat digeneralisasi ke destinasi lain.\n",
|
||
"- **Bahasa Campuran & Sarkasme:** Model berbasis TF-IDF kesulitan menangani ulasan yang menggunakan campuran bahasa (Indonesia-Inggris) atau mengandung sarkasme/ironi.\n",
|
||
"- **Ukuran Data:** Meskipun 3.500 ulasan cukup baik, dataset yang lebih besar dapat meningkatkan robustisitas model.\n",
|
||
"- **Ketergantungan TF-IDF:** Representasi fitur ini tidak menangkap makna semantik atau urutan kata secara mendalam.\n",
|
||
"\n",
|
||
"**Pekerjaan Lanjutan:**\n",
|
||
"- **Aspect-Based Sentiment Analysis (ABSA):** Menganalisis sentimen untuk setiap aspek spesifik dalam satu ulasan (misal: \"parkir (negatif), bangunan (positif)\").\n",
|
||
"- **Fine-tuning Model Bahasa:** Menggunakan model Transformer seperti IndoBERT atau BERT-large yang di-*fine-tune* pada dataset ini untuk performa yang lebih tinggi.\n",
|
||
"- **Deteksi Sarkasme:** Mengembangkan model khusus untuk mengidentifikasi sarkasme, yang dapat meningkatkan akurasi klasifikasi sentimen.\n",
|
||
"- **Active Learning:** Menggunakan model saat ini untuk mengidentifikasi ulasan yang paling ambigu, lalu melabelinya secara manual untuk memperkaya dataset secara efisien."
|
||
]
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"source": [
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"### 27. Lampiran\n",
|
||
"\n",
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||
"**Skema Folder Proyek:**\n",
|
||
"```\n",
|
||
"lawang_sewu_analysis/\n",
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||
"├── artefak/ # Folder untuk output model, data, dan visualisasi\n",
|
||
"│ ├── sentimen_tfidf_linearsvc.pkl\n",
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||
"│ ├── dataset_clean.csv\n",
|
||
"│ ├── run_metadata.json\n",
|
||
"│ └── lda_topics.html\n",
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||
"├── data/ # Folder untuk data mentah\n",
|
||
"│ └── LabeledLawangSewuReviewData.xlsx\n",
|
||
"└── Analisis_Sentimen_Lawang_Sewu.ipynb # Notebook utama ini\n",
|
||
"```\n",
|
||
"\n",
|
||
"**Spesifikasi & Versi Pustaka:**\n",
|
||
"- Lihat output dari sel kode di **Bagian 4** untuk detail versi pustaka yang digunakan dalam proses ini."
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