Upload File Teknis Skripsi
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**LIBARRY**"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"import librosa\n",
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"import numpy as np\n",
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"import os\n",
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"import joblib\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**PATH DATASET**"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Path dataset hasil preprocessing\n",
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"DATASET_PATH = \"preprocessing_data_v2\"\n",
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"OUTPUT_FOLDER = \"E:\\! KULIAHHH\\Ivano Kuliah\\!SEMESTER 8\\!SKRIPSI\\Data Suara\\!REVISI\\hasil_ekstrak_mfcc_v2\"\n",
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"\n",
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"# Buat folder output jika belum ada\n",
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"os.makedirs(OUTPUT_FOLDER, exist_ok=True)\n",
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"\n",
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"# Path penyimpanan file hasil ekstraksi\n",
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"OUTPUT_FEATURES = os.path.join(OUTPUT_FOLDER, \"X_train.npy\")\n",
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"OUTPUT_LABELS = os.path.join(OUTPUT_FOLDER, \"y_train.npy\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**PARAMETER MFCC**"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Parameter MFCC\n",
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"SAMPLE_RATE = 48000\n",
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"N_MFCC = 40 # Jumlah koefisien MFCC yang diambil\n",
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"N_FFT = 4096 # Panjang FFT\n",
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"HOP_LENGTH = 512 # Hop size\n",
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"\n",
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"X = [] # Menyimpan fitur MFCC\n",
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"y = [] # Menyimpan label kata"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**FUNGSI EKSTRASI MFCC**"
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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": 12,
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"metadata": {},
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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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|
||||
"Ekstraksi MFCC selesai. Dataset tersimpan di folder 'E:\\! KULIAHHH\\Ivano Kuliah\\!SEMESTER 8\\!SKRIPSI\\Data Suara\\!REVISI\\hasil_ekstrak_mfcc_v2'.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def extract_mfcc(file_path):\n",
|
||||
" \"\"\"Ekstraksi fitur MFCC dari file audio.\"\"\"\n",
|
||||
" y_audio, sr = librosa.load(file_path, sr=SAMPLE_RATE)\n",
|
||||
" mfcc = librosa.feature.mfcc(y=y_audio, sr=sr, n_mfcc=N_MFCC, n_fft=N_FFT, hop_length=HOP_LENGTH, lifter=22)\n",
|
||||
" return np.mean(mfcc, axis=1) # Rata-rata per koefisien\n",
|
||||
"\n",
|
||||
"file_names = [] # Tambahan untuk menyimpan nama file\n",
|
||||
"\n",
|
||||
"# Looping folder Positif dan Negatif\n",
|
||||
"for label_folder in os.listdir(DATASET_PATH):\n",
|
||||
" label_path = os.path.join(DATASET_PATH, label_folder)\n",
|
||||
"\n",
|
||||
" if os.path.isdir(label_path):\n",
|
||||
" for file_name in os.listdir(label_path):\n",
|
||||
" if file_name.endswith(\".wav\"):\n",
|
||||
" file_path = os.path.join(label_path, file_name)\n",
|
||||
" mfcc_features = extract_mfcc(file_path)\n",
|
||||
"\n",
|
||||
" X.append(mfcc_features)\n",
|
||||
" y.append(label_folder)\n",
|
||||
" file_names.append(f\"{label_folder}/{file_name}\") # Simpan dengan labelnya biar mudah dilacak\n",
|
||||
"\n",
|
||||
" print(f\"Processed: {label_folder}/{file_name} → Label: {label_folder}\")\n",
|
||||
"\n",
|
||||
"# Konversi ke array numpy\n",
|
||||
"X = np.array(X)\n",
|
||||
"y = np.array(y)\n",
|
||||
"\n",
|
||||
"# Simpan hasil\n",
|
||||
"np.save(OUTPUT_FEATURES, X)\n",
|
||||
"np.save(OUTPUT_LABELS, y)\n",
|
||||
"np.save(os.path.join(OUTPUT_FOLDER, \"file_names.npy\"), np.array(file_names)) # Tambahan\n",
|
||||
"\n",
|
||||
"print(f\"Ekstraksi MFCC selesai. Dataset tersimpan di folder '{OUTPUT_FOLDER}'.\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,444 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**LIBRARY**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import librosa\n",
|
||||
"import librosa.display\n",
|
||||
"import numpy as np\n",
|
||||
"import noisereduce as nr\n",
|
||||
"import os\n",
|
||||
"import soundfile as sf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**PARAMETER**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Parameter\n",
|
||||
"TARGET_DURATION = 1.0 # Durasi target dalam detik\n",
|
||||
"SAMPLE_RATE = 48000 # Frekuensi sampling sesuai dataset (48 kHz)\n",
|
||||
"DATASET_PATH = \"EDITED3\" # Ganti dengan path dataset\n",
|
||||
"OUTPUT_DIR = \"E:\\! KULIAHHH\\Ivano Kuliah\\!SEMESTER 8\\!SKRIPSI\\Data Suara\\!REVISI\\preprocessing_data_v2\" # Folder untuk menyimpan hasil preprocessing\n",
|
||||
"\n",
|
||||
"# Pastikan folder output ada\n",
|
||||
"os.makedirs(OUTPUT_DIR, exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**LOAD DATASET**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def load_audio(file_path, sr=SAMPLE_RATE):\n",
|
||||
" \"\"\"Membaca file audio dan mengembalikan waveform serta sample rate.\"\"\"\n",
|
||||
" y, sr = librosa.load(file_path, sr=sr)\n",
|
||||
" return y, sr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**NORMALISASI AUDIO**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def normalize_audio(y):\n",
|
||||
" \"\"\"Normalisasi amplitudo antara -1 dan 1.\"\"\"\n",
|
||||
" return y / np.max(np.abs(y))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**PADDING AUDIO ATAU CUT**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def pad_audio(y, sr=SAMPLE_RATE, target_duration=TARGET_DURATION):\n",
|
||||
" \"\"\"Menyesuaikan durasi audio dengan padding atau pemotongan.\"\"\"\n",
|
||||
" target_length = int(sr * target_duration) # Hitung jumlah sampel target\n",
|
||||
" if len(y) > target_length:\n",
|
||||
" return y[:target_length] # Potong jika lebih panjang\n",
|
||||
" else:\n",
|
||||
" return np.pad(y, (0, target_length - len(y)), mode='constant') # Tambahkan padding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**NOISE REDUCTION**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def reduce_noise(y, sr=SAMPLE_RATE):\n",
|
||||
" \"\"\"Mengurangi noise dengan noisereduce.\"\"\"\n",
|
||||
" return nr.reduce_noise(y=y, sr=sr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**PREPROCESS AUDIO**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Processed: Negatif/mindrabodoh1 - Copy.wav\n",
|
||||
"Processed: Negatif/mindrabodoh1.wav\n",
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
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||||
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|
||||
"Preprocessing selesai. Hasil disimpan di folder 'preprocessing_data'.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def preprocess_audio(file_path, output_path):\n",
|
||||
" \"\"\"Melakukan preprocessing (normalisasi, padding, noise reduction) pada file audio.\"\"\"\n",
|
||||
" y, sr = load_audio(file_path)\n",
|
||||
" y = reduce_noise(y, sr)\n",
|
||||
" y = normalize_audio(y)\n",
|
||||
" y = pad_audio(y, sr)\n",
|
||||
" \n",
|
||||
" # Simpan hasil preprocessing\n",
|
||||
" sf.write(output_path, y, sr)\n",
|
||||
"\n",
|
||||
"# Looping folder Positif dan Negatif\n",
|
||||
"for label_folder in os.listdir(DATASET_PATH):\n",
|
||||
" label_path = os.path.join(DATASET_PATH, label_folder)\n",
|
||||
" \n",
|
||||
" if os.path.isdir(label_path): # Pastikan itu folder Positif / Negatif\n",
|
||||
" output_label_path = os.path.join(OUTPUT_DIR, label_folder)\n",
|
||||
" os.makedirs(output_label_path, exist_ok=True)\n",
|
||||
"\n",
|
||||
" # Looping file WAV di dalam folder\n",
|
||||
" for file_name in os.listdir(label_path):\n",
|
||||
" if file_name.endswith(\".wav\"):\n",
|
||||
" input_path = os.path.join(label_path, file_name)\n",
|
||||
" output_path = os.path.join(output_label_path, file_name)\n",
|
||||
" preprocess_audio(input_path, output_path)\n",
|
||||
" print(f\"Processed: {label_folder}/{file_name}\")\n",
|
||||
"\n",
|
||||
"print(\"Preprocessing selesai. Hasil disimpan di folder 'preprocessing_data'.\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,181 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import seaborn as sns\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import sklearn.preprocessing as pre\n",
|
||||
"from sklearn.model_selection import train_test_split, StratifiedKFold\n",
|
||||
"from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
|
||||
"import joblib"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X = np.load(\"E:\\! KULIAHHH\\Ivano Kuliah\\!SEMESTER 8\\!SKRIPSI\\Data Suara\\!REVISI\\hasil_ekstrak_mfcc_v2/X_train.npy\")\n",
|
||||
"y = np.load(\"E:\\! KULIAHHH\\Ivano Kuliah\\!SEMESTER 8\\!SKRIPSI\\Data Suara\\!REVISI\\hasil_ekstrak_mfcc_v2/y_train.npy\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Encode label menjadi angka\n",
|
||||
"label_encoder = pre.LabelEncoder()\n",
|
||||
"y_encoded = label_encoder.fit_transform(y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Inisialisasi K-Fold Cross-Validation\n",
|
||||
"k = 5 # Jumlah fold\n",
|
||||
"kf = StratifiedKFold(n_splits=k, shuffle=True, random_state=42)\n",
|
||||
"accuracies = [] # Simpan akurasi tiap fold"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class LVQ:\n",
|
||||
" def __init__(self, n_classes, n_prototypes=2, learning_rate=0.01, epochs=200):\n",
|
||||
" self.n_classes = n_classes\n",
|
||||
" self.n_prototypes = n_prototypes\n",
|
||||
" self.learning_rate = learning_rate\n",
|
||||
" self.epochs = epochs\n",
|
||||
" self.prototypes = None\n",
|
||||
" self.prototype_labels = None\n",
|
||||
"\n",
|
||||
" def fit(self, X, y):\n",
|
||||
" np.random.seed(42)\n",
|
||||
" self.prototypes = []\n",
|
||||
" self.prototype_labels = []\n",
|
||||
"\n",
|
||||
" for label in np.unique(y):\n",
|
||||
" idx = np.where(y == label)[0]\n",
|
||||
" chosen = np.random.choice(idx, self.n_prototypes, replace=False)\n",
|
||||
" self.prototypes.extend(X[chosen])\n",
|
||||
" self.prototype_labels.extend([label] * self.n_prototypes)\n",
|
||||
"\n",
|
||||
" self.prototypes = np.array(self.prototypes)\n",
|
||||
" self.prototype_labels = np.array(self.prototype_labels)\n",
|
||||
"\n",
|
||||
" for epoch in range(self.epochs):\n",
|
||||
" for i in range(len(X)):\n",
|
||||
" sample = X[i]\n",
|
||||
" label = y[i]\n",
|
||||
" distances = np.linalg.norm(self.prototypes - sample, axis=1)\n",
|
||||
" winner_idx = np.argmin(distances)\n",
|
||||
"\n",
|
||||
" if self.prototype_labels[winner_idx] == label:\n",
|
||||
" self.prototypes[winner_idx] += self.learning_rate * (sample - self.prototypes[winner_idx])\n",
|
||||
" else:\n",
|
||||
" self.prototypes[winner_idx] -= self.learning_rate * (sample - self.prototypes[winner_idx])\n",
|
||||
"\n",
|
||||
" self.learning_rate *= 0.95\n",
|
||||
"\n",
|
||||
" def predict(self, X):\n",
|
||||
" y_pred = []\n",
|
||||
" for sample in X:\n",
|
||||
" distances = np.linalg.norm(self.prototypes - sample, axis=1)\n",
|
||||
" winner_idx = np.argmin(distances)\n",
|
||||
" y_pred.append(self.prototype_labels[winner_idx])\n",
|
||||
" return np.array(y_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"🔄 Fold 1/5...\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"✅ Akurasi Fold 1: 79.59%\n",
|
||||
"🔄 Fold 2/5...\n",
|
||||
"✅ Akurasi Fold 2: 87.76%\n",
|
||||
"🔄 Fold 3/5...\n",
|
||||
"✅ Akurasi Fold 3: 77.08%\n",
|
||||
"🔄 Fold 4/5...\n",
|
||||
"✅ Akurasi Fold 4: 83.33%\n",
|
||||
"🔄 Fold 5/5...\n",
|
||||
"✅ Akurasi Fold 5: 70.83%\n",
|
||||
"\n",
|
||||
"📊 Rata-rata Akurasi dari 5-Fold Cross Validation: 79.72%\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# K-Fold Cross-Validation\n",
|
||||
"for fold, (train_idx, test_idx) in enumerate(kf.split(X, y_encoded)):\n",
|
||||
" print(f\"🔄 Fold {fold+1}/{k}...\")\n",
|
||||
"\n",
|
||||
" # Split data untuk fold ini\n",
|
||||
" X_train, X_test = X[train_idx], X[test_idx]\n",
|
||||
" y_train, y_test = y_encoded[train_idx], y_encoded[test_idx]\n",
|
||||
"\n",
|
||||
" # Inisialisasi dan training model LVQ\n",
|
||||
" lvq_model = LVQ(n_classes=len(set(y_encoded)), n_prototypes=2, learning_rate=0.01, epochs=200)\n",
|
||||
" lvq_model.fit(X_train, y_train)\n",
|
||||
"\n",
|
||||
" # Prediksi data uji\n",
|
||||
" y_pred = lvq_model.predict(X_test)\n",
|
||||
"\n",
|
||||
" # Evaluasi\n",
|
||||
" accuracy = accuracy_score(y_test, y_pred)\n",
|
||||
" accuracies.append(accuracy)\n",
|
||||
" print(f\"✅ Akurasi Fold {fold+1}: {accuracy * 100:.2f}%\")\n",
|
||||
"\n",
|
||||
"# Hitung rata-rata akurasi dari semua fold\n",
|
||||
"mean_accuracy = np.mean(accuracies)\n",
|
||||
"print(f\"\\n📊 Rata-rata Akurasi dari {k}-Fold Cross Validation: {mean_accuracy * 100:.2f}%\")\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
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