2854 lines
106 KiB
Plaintext
2854 lines
106 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 73
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||
},
|
||
"id": "EMnX7B87MgpN",
|
||
"outputId": "14ed4621-d746-48a9-a97a-f9c96958d1f2"
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},
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"outputs": [
|
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{
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"data": {
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"text/html": [
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"\n",
|
||
" <input type=\"file\" id=\"files-9ea630b8-f055-4575-8ebd-97b8f1c3abbb\" name=\"files[]\" multiple disabled\n",
|
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" style=\"border:none\" />\n",
|
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" <output id=\"result-9ea630b8-f055-4575-8ebd-97b8f1c3abbb\">\n",
|
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" Upload widget is only available when the cell has been executed in the\n",
|
||
" current browser session. Please rerun this cell to enable.\n",
|
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" </output>\n",
|
||
" <script>// Copyright 2017 Google LLC\n",
|
||
"//\n",
|
||
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
|
||
"// you may not use this file except in compliance with the License.\n",
|
||
"// You may obtain a copy of the License at\n",
|
||
"//\n",
|
||
"// http://www.apache.org/licenses/LICENSE-2.0\n",
|
||
"//\n",
|
||
"// Unless required by applicable law or agreed to in writing, software\n",
|
||
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
|
||
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
|
||
"// See the License for the specific language governing permissions and\n",
|
||
"// limitations under the License.\n",
|
||
"\n",
|
||
"/**\n",
|
||
" * @fileoverview Helpers for google.colab Python module.\n",
|
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" */\n",
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"(function(scope) {\n",
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"function span(text, styleAttributes = {}) {\n",
|
||
" const element = document.createElement('span');\n",
|
||
" element.textContent = text;\n",
|
||
" for (const key of Object.keys(styleAttributes)) {\n",
|
||
" element.style[key] = styleAttributes[key];\n",
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" }\n",
|
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" return element;\n",
|
||
"}\n",
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"\n",
|
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"// Max number of bytes which will be uploaded at a time.\n",
|
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"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
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"\n",
|
||
"function _uploadFiles(inputId, outputId) {\n",
|
||
" const steps = uploadFilesStep(inputId, outputId);\n",
|
||
" const outputElement = document.getElementById(outputId);\n",
|
||
" // Cache steps on the outputElement to make it available for the next call\n",
|
||
" // to uploadFilesContinue from Python.\n",
|
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" outputElement.steps = steps;\n",
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"\n",
|
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" return _uploadFilesContinue(outputId);\n",
|
||
"}\n",
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"\n",
|
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"// This is roughly an async generator (not supported in the browser yet),\n",
|
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"// where there are multiple asynchronous steps and the Python side is going\n",
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"// to poll for completion of each step.\n",
|
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"// This uses a Promise to block the python side on completion of each step,\n",
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"// then passes the result of the previous step as the input to the next step.\n",
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"function _uploadFilesContinue(outputId) {\n",
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" const outputElement = document.getElementById(outputId);\n",
|
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" const steps = outputElement.steps;\n",
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"\n",
|
||
" const next = steps.next(outputElement.lastPromiseValue);\n",
|
||
" return Promise.resolve(next.value.promise).then((value) => {\n",
|
||
" // Cache the last promise value to make it available to the next\n",
|
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" // step of the generator.\n",
|
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" outputElement.lastPromiseValue = value;\n",
|
||
" return next.value.response;\n",
|
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" });\n",
|
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"}\n",
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"\n",
|
||
"/**\n",
|
||
" * Generator function which is called between each async step of the upload\n",
|
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" * process.\n",
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" * @param {string} inputId Element ID of the input file picker element.\n",
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" * @param {string} outputId Element ID of the output display.\n",
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" * @return {!Iterable<!Object>} Iterable of next steps.\n",
|
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" */\n",
|
||
"function* uploadFilesStep(inputId, outputId) {\n",
|
||
" const inputElement = document.getElementById(inputId);\n",
|
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" inputElement.disabled = false;\n",
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"\n",
|
||
" const outputElement = document.getElementById(outputId);\n",
|
||
" outputElement.innerHTML = '';\n",
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"\n",
|
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" const pickedPromise = new Promise((resolve) => {\n",
|
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" inputElement.addEventListener('change', (e) => {\n",
|
||
" resolve(e.target.files);\n",
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" });\n",
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" });\n",
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||
"\n",
|
||
" const cancel = document.createElement('button');\n",
|
||
" inputElement.parentElement.appendChild(cancel);\n",
|
||
" cancel.textContent = 'Cancel upload';\n",
|
||
" const cancelPromise = new Promise((resolve) => {\n",
|
||
" cancel.onclick = () => {\n",
|
||
" resolve(null);\n",
|
||
" };\n",
|
||
" });\n",
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"\n",
|
||
" // Wait for the user to pick the files.\n",
|
||
" const files = yield {\n",
|
||
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
|
||
" response: {\n",
|
||
" action: 'starting',\n",
|
||
" }\n",
|
||
" };\n",
|
||
"\n",
|
||
" cancel.remove();\n",
|
||
"\n",
|
||
" // Disable the input element since further picks are not allowed.\n",
|
||
" inputElement.disabled = true;\n",
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||
"\n",
|
||
" if (!files) {\n",
|
||
" return {\n",
|
||
" response: {\n",
|
||
" action: 'complete',\n",
|
||
" }\n",
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||
" };\n",
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" }\n",
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"\n",
|
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" for (const file of files) {\n",
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" const li = document.createElement('li');\n",
|
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" li.append(span(file.name, {fontWeight: 'bold'}));\n",
|
||
" li.append(span(\n",
|
||
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
|
||
" `last modified: ${\n",
|
||
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
|
||
" 'n/a'} - `));\n",
|
||
" const percent = span('0% done');\n",
|
||
" li.appendChild(percent);\n",
|
||
"\n",
|
||
" outputElement.appendChild(li);\n",
|
||
"\n",
|
||
" const fileDataPromise = new Promise((resolve) => {\n",
|
||
" const reader = new FileReader();\n",
|
||
" reader.onload = (e) => {\n",
|
||
" resolve(e.target.result);\n",
|
||
" };\n",
|
||
" reader.readAsArrayBuffer(file);\n",
|
||
" });\n",
|
||
" // Wait for the data to be ready.\n",
|
||
" let fileData = yield {\n",
|
||
" promise: fileDataPromise,\n",
|
||
" response: {\n",
|
||
" action: 'continue',\n",
|
||
" }\n",
|
||
" };\n",
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||
"\n",
|
||
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
|
||
" let position = 0;\n",
|
||
" do {\n",
|
||
" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
|
||
" const chunk = new Uint8Array(fileData, position, length);\n",
|
||
" position += length;\n",
|
||
"\n",
|
||
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
|
||
" yield {\n",
|
||
" response: {\n",
|
||
" action: 'append',\n",
|
||
" file: file.name,\n",
|
||
" data: base64,\n",
|
||
" },\n",
|
||
" };\n",
|
||
"\n",
|
||
" let percentDone = fileData.byteLength === 0 ?\n",
|
||
" 100 :\n",
|
||
" Math.round((position / fileData.byteLength) * 100);\n",
|
||
" percent.textContent = `${percentDone}% done`;\n",
|
||
"\n",
|
||
" } while (position < fileData.byteLength);\n",
|
||
" }\n",
|
||
"\n",
|
||
" // All done.\n",
|
||
" yield {\n",
|
||
" response: {\n",
|
||
" action: 'complete',\n",
|
||
" }\n",
|
||
" };\n",
|
||
"}\n",
|
||
"\n",
|
||
"scope.google = scope.google || {};\n",
|
||
"scope.google.colab = scope.google.colab || {};\n",
|
||
"scope.google.colab._files = {\n",
|
||
" _uploadFiles,\n",
|
||
" _uploadFilesContinue,\n",
|
||
"};\n",
|
||
"})(self);\n",
|
||
"</script> "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saving ulasan_balanced.csv to ulasan_balanced.csv\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 📌 STEP 1: Upload File\n",
|
||
"from google.colab import files # Gunakan ini kalau di Google Colab\n",
|
||
"uploaded = files.upload()\n",
|
||
"\n",
|
||
"import pandas as pd"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "0TZG8_QDMhUM",
|
||
"outputId": "b52f732e-7f0a-4816-e8ea-a66a52def34f"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Jumlah total data: 801\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Baca file yang diupload\n",
|
||
"df = pd.read_csv(list(uploaded.keys())[0])\n",
|
||
"print(\"Jumlah total data:\", df.shape[0])\n",
|
||
"df = df[['content', 'sentimen']] # pastikan kolom ini tersedia"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"background_save": true,
|
||
"base_uri": "https://localhost:8080/",
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"height": 941,
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"referenced_widgets": [
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},
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"id": "VK1jOCnsMkmu",
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||
"outputId": "d9190c46-c62b-4788-d623-0d450b41c1ff"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.52.4)\n",
|
||
"Collecting nlpaug\n",
|
||
" Downloading nlpaug-1.1.11-py3-none-any.whl.metadata (14 kB)\n",
|
||
"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.18.0)\n",
|
||
"Requirement already satisfied: huggingface-hub<1.0,>=0.30.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.32.4)\n",
|
||
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2.0.2)\n",
|
||
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (24.2)\n",
|
||
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (6.0.2)\n",
|
||
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2024.11.6)\n",
|
||
"Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers) (2.32.3)\n",
|
||
"Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.21.1)\n",
|
||
"Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.5.3)\n",
|
||
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers) (4.67.1)\n",
|
||
"Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from nlpaug) (2.2.2)\n",
|
||
"Requirement already satisfied: gdown>=4.0.0 in /usr/local/lib/python3.11/dist-packages (from nlpaug) (5.2.0)\n",
|
||
"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from gdown>=4.0.0->nlpaug) (4.13.4)\n",
|
||
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (2025.3.2)\n",
|
||
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (4.14.0)\n",
|
||
"Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.30.0->transformers) (1.1.2)\n",
|
||
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2.0->nlpaug) (2.9.0.post0)\n",
|
||
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2.0->nlpaug) (2025.2)\n",
|
||
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2.0->nlpaug) (2025.2)\n",
|
||
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.4.2)\n",
|
||
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.10)\n",
|
||
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2.4.0)\n",
|
||
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2025.4.26)\n",
|
||
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=1.2.0->nlpaug) (1.17.0)\n",
|
||
"Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4->gdown>=4.0.0->nlpaug) (2.7)\n",
|
||
"Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.11/dist-packages (from requests[socks]->gdown>=4.0.0->nlpaug) (1.7.1)\n",
|
||
"Downloading nlpaug-1.1.11-py3-none-any.whl (410 kB)\n",
|
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.5/410.5 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25hInstalling collected packages: nlpaug\n",
|
||
"Successfully installed nlpaug-1.1.11\n",
|
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"Train: 640, Test: 120, Val: 41\n"
|
||
]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
||
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
|
||
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
||
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
||
"You will be able to reuse this secret in all of your notebooks.\n",
|
||
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
||
" warnings.warn(\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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{
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"data": {
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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": "49e7aa892a304a17ab76ee99b835f30d",
|
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"version_major": 2,
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"metadata": {},
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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": "027a5a41b42f459eb7f3539a45b59713",
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"version_major": 2,
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"metadata": {},
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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": "34dc529069d14f3982a4ab985cf111dc",
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"version_major": 2,
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]
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"metadata": {},
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|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"The following layers were not sharded: encoder.layer.*.output.LayerNorm.bias, encoder.layer.*.attention.output.dense.bias, encoder.layer.*.output.dense.bias, encoder.layer.*.attention.self.key.weight, encoder.layer.*.attention.self.query.weight, embeddings.word_embeddings.weight, encoder.layer.*.intermediate.dense.weight, embeddings.token_type_embeddings.weight, encoder.layer.*.attention.self.key.bias, encoder.layer.*.intermediate.dense.bias, embeddings.position_embeddings.weight, encoder.layer.*.attention.self.value.weight, encoder.layer.*.output.dense.weight, encoder.layer.*.attention.output.LayerNorm.weight, embeddings.LayerNorm.bias, encoder.layer.*.attention.output.LayerNorm.bias, encoder.layer.*.output.LayerNorm.weight, encoder.layer.*.attention.self.query.bias, encoder.layer.*.attention.self.value.bias, embeddings.LayerNorm.weight, encoder.layer.*.attention.output.dense.weight\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "58444e78b25840978b90b7ac64e58d00",
|
||
"version_major": 2,
|
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"version_minor": 0
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]
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},
|
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"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Install dependencies jika belum\n",
|
||
"!pip install transformers nlpaug\n",
|
||
"\n",
|
||
"# Import\n",
|
||
"import nlpaug.augmenter.word as naw\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"# Split dataset\n",
|
||
"df_temp, df_val = train_test_split(df, test_size=0.05, random_state=42, stratify=df['sentimen'])\n",
|
||
"test_size_adjusted = 0.15 / (1 - 0.05)\n",
|
||
"df_train, df_test = train_test_split(df_temp, test_size=test_size_adjusted, random_state=42, stratify=df_temp['sentimen'])\n",
|
||
"\n",
|
||
"print(f\"Train: {df_train.shape[0]}, Test: {df_test.shape[0]}, Val: {df_val.shape[0]}\")\n",
|
||
"\n",
|
||
"# Load IndoBERT augmenter\n",
|
||
"aug = naw.ContextualWordEmbsAug(\n",
|
||
" model_path='indobenchmark/indobert-base-p1',\n",
|
||
" action=\"insert\",\n",
|
||
" top_k=10,\n",
|
||
" device='cpu'\n",
|
||
")\n",
|
||
"\n",
|
||
"# Fungsi augmentasi lebih banyak\n",
|
||
"def augment_text(text, n=5): # <- di sini kamu bisa set mau augment berapa kali\n",
|
||
" try:\n",
|
||
" augmented = aug.augment(text, n=n)\n",
|
||
" return augmented if isinstance(augmented, list) else [augmented]\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"Error augmenting: {e}\")\n",
|
||
" return []\n",
|
||
"\n",
|
||
"# Lakukan augmentasi\n",
|
||
"augmented_rows = []\n",
|
||
"for idx, row in df_train.iterrows():\n",
|
||
" aug_texts = augment_text(row['content'], n=5) # <-- augment 5x per data\n",
|
||
" for aug_text in aug_texts:\n",
|
||
" augmented_rows.append({'content': aug_text, 'sentimen': row['sentimen']})\n",
|
||
" if idx % 500 == 0:\n",
|
||
" print(f\"Augmenting row {idx}/{df_train.shape[0]}\")\n",
|
||
"\n",
|
||
"# Gabungkan hasil augmentasi\n",
|
||
"df_augmented = pd.DataFrame(augmented_rows)\n",
|
||
"df_train_augmented = pd.concat([df_train, df_augmented], ignore_index=True)\n",
|
||
"\n",
|
||
"print(f\"Jumlah data setelah augmentasi: {df_train_augmented.shape[0]}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "036vmokdOYnd"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"pip install Sastrawi"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "yYi1S6Y1OZcd"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
|
||
"factory = StemmerFactory()\n",
|
||
"stemmer = factory.create_stemmer()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "zV0pVMvNMo0I"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 📌 STEP 3: Preprocessing Teks (SETELAH AUGMENTASI)\n",
|
||
"import re\n",
|
||
"import nltk\n",
|
||
"nltk.download('stopwords')\n",
|
||
"from nltk.corpus import stopwords\n",
|
||
"\n",
|
||
"stop_words = set(stopwords.words('indonesian'))\n",
|
||
"\n",
|
||
"def clean_text(text):\n",
|
||
" text = str(text).lower()\n",
|
||
" text = re.sub(r'http\\S+|www\\S+', '', text) # hapus URL\n",
|
||
" text = re.sub(r'[^a-zA-Z\\s]', '', text) # hapus simbol, angka, dll\n",
|
||
" text = re.sub(r'[\\U00010000-\\U0010ffff]', '', text) # hapus emoji\n",
|
||
" words = text.split()\n",
|
||
" words = [w for w in words if w not in stop_words and len(w) > 2]\n",
|
||
" return words\n",
|
||
"\n",
|
||
"# Terapkan ke semua bagian\n",
|
||
"df_train_augmented['clean'] = df_train_augmented['content'].apply(clean_text)\n",
|
||
"df_test['clean'] = df_test['content'].apply(clean_text)\n",
|
||
"df_val['clean'] = df_val['content'].apply(clean_text)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "62OU3kQ0MttG"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Gabungkan token hasil cleaning menjadi string\n",
|
||
"df_train_augmented['clean_str'] = df_train_augmented['clean'].apply(lambda x: ' '.join(x))\n",
|
||
"df_test['clean_str'] = df_test['clean'].apply(lambda x: ' '.join(x))\n",
|
||
"df_val['clean_str'] = df_val['clean'].apply(lambda x: ' '.join(x))\n",
|
||
"\n",
|
||
"# TF-IDF Vectorizer\n",
|
||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||
"\n",
|
||
"vectorizer = TfidfVectorizer()\n",
|
||
"X_train = vectorizer.fit_transform(df_train_augmented['clean_str'])\n",
|
||
"X_test = vectorizer.transform(df_test['clean_str'])\n",
|
||
"X_val = vectorizer.transform(df_val['clean_str'])\n",
|
||
"\n",
|
||
"# Target labels\n",
|
||
"y_train = df_train_augmented['sentimen']\n",
|
||
"y_test = df_test['sentimen']\n",
|
||
"y_val = df_val['sentimen']\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "U5f-DKCSMvHt"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 📌 STEP 5: Latih Naive Bayes\n",
|
||
"from sklearn.naive_bayes import MultinomialNB\n",
|
||
"from sklearn.metrics import classification_report, accuracy_score\n",
|
||
"\n",
|
||
"model = MultinomialNB()\n",
|
||
"model.fit(X_train, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "4QWA5b0XMwNF"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 📌 STEP 6: Evaluasi\n",
|
||
"print(\"\\n📊 Evaluasi pada Test Set:\")\n",
|
||
"y_pred_test = model.predict(X_test)\n",
|
||
"print(\"Akurasi:\", accuracy_score(y_test, y_pred_test))\n",
|
||
"print(classification_report(y_test, y_pred_test))\n",
|
||
"\n",
|
||
"print(\"\\n📊 Evaluasi pada Validation Set:\")\n",
|
||
"y_pred_val = model.predict(X_val)\n",
|
||
"print(\"Akurasi:\", accuracy_score(y_val, y_pred_val))\n",
|
||
"print(classification_report(y_val, y_pred_val))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "Ax79G3jticm8"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import joblib\n",
|
||
"joblib.dump(model, 'model_nb.pkl')\n",
|
||
"joblib.dump(vectorizer, 'tfidf_vectorizer.pkl')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "JMNNKXcXM2kJ"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from sklearn.metrics import confusion_matrix\n",
|
||
"from wordcloud import WordCloud\n",
|
||
"\n",
|
||
"# CONFUSION MATRIX - Test\n",
|
||
"cm_test = confusion_matrix(y_test, y_pred_test, labels=model.classes_)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(7, 5))\n",
|
||
"sns.heatmap(cm_test, annot=True, fmt='d', cmap='Blues', xticklabels=model.classes_, yticklabels=model.classes_)\n",
|
||
"plt.title('Confusion Matrix - Test Set')\n",
|
||
"plt.xlabel('Predicted Label')\n",
|
||
"plt.ylabel('True Label')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# CONFUSION MATRIX - Validation\n",
|
||
"cm_val = confusion_matrix(y_val, y_pred_val, labels=model.classes_)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(7, 5))\n",
|
||
"sns.heatmap(cm_val, annot=True, fmt='d', cmap='Greens', xticklabels=model.classes_, yticklabels=model.classes_)\n",
|
||
"plt.title('Confusion Matrix - Validation Set')\n",
|
||
"plt.xlabel('Predicted Label')\n",
|
||
"plt.ylabel('True Label')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# DISTRIBUSI PREDIKSI - Test\n",
|
||
"plt.figure(figsize=(6, 4))\n",
|
||
"sns.countplot(x=y_pred_test, order=model.classes_, palette='viridis')\n",
|
||
"plt.title('Distribusi Prediksi - Test Set')\n",
|
||
"plt.xlabel('Label Prediksi')\n",
|
||
"plt.ylabel('Jumlah')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# WORD CLOUD PER SENTIMEN (opsional & keren)\n",
|
||
"from wordcloud import WordCloud\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"sentimen_list = ['positif', 'negatif', 'netral']\n",
|
||
"for label in sentimen_list:\n",
|
||
" # Gabungkan token jadi string per baris → lalu semua baris jadi satu string\n",
|
||
" text = \" \".join(df_train_augmented[df_train_augmented['sentimen'] == label]['clean'].apply(lambda x: ' '.join(x)))\n",
|
||
"\n",
|
||
" wc = WordCloud(width=800, height=400, background_color='white').generate(text)\n",
|
||
" plt.figure(figsize=(10, 5))\n",
|
||
" plt.imshow(wc, interpolation='bilinear')\n",
|
||
" plt.axis('off')\n",
|
||
" plt.title(f'Word Cloud - {label.upper()}')\n",
|
||
" plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "ZJj_FGsiNLka"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Plot distribusi sentimen dalam train set\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"plt.figure(figsize=(6, 4))\n",
|
||
"sns.countplot(x=df_train['sentimen'], order=df_train_augmented['sentimen'].value_counts().index, palette='Set2')\n",
|
||
"plt.title(\"Distribusi Sentimen di Train Set\")\n",
|
||
"plt.xlabel(\"Sentimen\")\n",
|
||
"plt.ylabel(\"Jumlah Data\")\n",
|
||
"plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "RQHF89mmhH2L"
|
||
},
|
||
"source": [
|
||
"## save file"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "IEzeVAuJhP2s"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_train.to_csv(\"train_original.csv\", index=False)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "iZ1a01flNofP"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Simpan ke CSV versi augmented (3x lipat)\n",
|
||
"df_train_augmented.to_csv(\"train_augmented.csv\", index=False)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "HS0v7k4whJNt"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Simpan test dan val\n",
|
||
"df_test.to_csv(\"test.csv\", index=False)\n",
|
||
"df_val.to_csv(\"val.csv\", index=False)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"toc_visible": true,
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
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