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Xnuvers007/Word_Negatif_Pos_Net_Detection.ipynb

Created December 25, 2025 19:57
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Detection Word with value negative, positive or netral
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Word_Negatif_Pos_Net_Detection.ipynb
{
"cells": [
{
"cell_type": "markdown",
"source": [
"MENGGUNAKAN GPU BIASA\n",
"USING COMMON GPU"
],
"metadata": {
"id": "64ZXK7wGC0Xx"
}
},
{
"cell_type": "code",
"source": [
"!pip install transformers torch scikit-learn matplotlib seaborn -q"
],
"metadata": {
"id": "W9reyfuYfjIH"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from typing import List, Dict, Tuple\n",
"\n",
"class SentimentDataset(torch.utils.data.Dataset):\n",
" \"\"\"\n",
" Custom Dataset class untuk memproses data teks agar kompatibel dengan PyTorch/Transformers.\n",
" \"\"\"\n",
" def __init__(self, encodings: Dict, labels: List[int]):\n",
" self.encodings = encodings\n",
" self.labels = labels\n",
"\n",
" def __getitem__(self, idx: int) -> Dict:\n",
" item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
" item['labels'] = torch.tensor(self.labels[idx])\n",
" return item\n",
"\n",
" def __len__(self) -> int:\n",
" return len(self.labels)\n",
"\n",
"class SentimentAnalyzer:\n",
" \"\"\"\n",
" Class utama untuk menangani Pipeline: Load Data -> Train -> Evaluasi -> Prediksi\n",
" \"\"\"\n",
" def __init__(self, model_name: str = 'distilbert-base-uncased'):\n",
" self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
" print(f\"🔄 System initialized using: {self.device} (Professional Mode)\")\n",
"\n",
" self.tokenizer = DistilBertTokenizer.from_pretrained(model_name)\n",
" self.model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=2)\n",
" self.model.to(self.device)\n",
"\n",
" def prepare_data(self, texts: List[str], labels: List[int]):\n",
" \"\"\"Memecah data menjadi train/val dan melakukan tokenisasi.\"\"\"\n",
" train_texts, val_texts, train_labels, val_labels = train_test_split(texts, labels, test_size=0.2, random_state=42)\n",
"\n",
" train_encodings = self.tokenizer(train_texts, truncation=True, padding=True, max_length=64)\n",
" val_encodings = self.tokenizer(val_texts, truncation=True, padding=True, max_length=64)\n",
"\n",
" self.train_dataset = SentimentDataset(train_encodings, train_labels)\n",
" self.val_dataset = SentimentDataset(val_encodings, val_labels)\n",
" print(\"✅ Data preparation complete.\")\n",
"\n",
" def compute_metrics(self, pred) -> Dict:\n",
" \"\"\"Fungsi utilitas untuk menghitung metrik evaluasi standar industri.\"\"\"\n",
" labels = pred.label_ids\n",
" preds = pred.predictions.argmax(-1)\n",
" precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')\n",
" acc = accuracy_score(labels, preds)\n",
" return {\n",
" 'accuracy': acc,\n",
" 'f1': f1,\n",
" 'precision': precision,\n",
" 'recall': recall\n",
" }\n",
"\n",
" def train(self, output_dir: str = './results', epochs: int = 3):\n",
" \"\"\"Proses Fine-tuning model.\"\"\"\n",
" training_args = TrainingArguments(\n",
" output_dir=output_dir,\n",
" num_train_epochs=epochs,\n",
" per_device_train_batch_size=8, # Kecil agar muat di memori Colab\n",
" per_device_eval_batch_size=16,\n",
" warmup_steps=10,\n",
" weight_decay=0.01,\n",
" logging_dir='./logs',\n",
" logging_steps=10,\n",
" eval_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" load_best_model_at_end=True\n",
" )\n",
"\n",
" self.trainer = Trainer(\n",
" model=self.model,\n",
" args=training_args,\n",
" train_dataset=self.train_dataset,\n",
" eval_dataset=self.val_dataset,\n",
" compute_metrics=self.compute_metrics\n",
" )\n",
"\n",
" print(\"🚀 Starting training process...\")\n",
" self.trainer.train()\n",
" print(\"✅ Training finished.\")\n",
"\n",
" def predict(self, text: str) -> str:\n",
" \"\"\"Melakukan inferensi pada data baru.\"\"\"\n",
" inputs = self.tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True, max_length=64).to(self.device)\n",
" with torch.no_grad():\n",
" logits = self.model(**inputs).logits\n",
"\n",
" predicted_class_id = logits.argmax().item()\n",
" return \"POSITIF 😊\" if predicted_class_id == 1 else \"NEGATIF 😠\"\n",
"\n",
"if __name__ == \"__main__\":\n",
" # 1. Dummy Data (Simulasi Dataset Review Produk)\n",
" # Dalam kasus nyata, ini diload dari CSV/Database\n",
" data_texts = [\n",
" \"Produk ini sangat luar biasa, saya sangat suka!\", \"Pelayanan sangat buruk dan lambat.\",\n",
" \"Kualitas barang oke, pengiriman cepat.\", \"Tidak recommended, barang rusak saat sampai.\",\n",
" \"Sangat puas dengan hasilnya.\", \"Menyesal beli di sini, buang-buang uang.\",\n",
" \"Fiturnya lengkap dan mudah digunakan.\", \"Sering error dan crash aplikasinya.\",\n",
" \"Admin ramah banget, makasih ya.\", \"Respon penjual jutek banget.\"\n",
" ] * 10 # Kita duplikasi agar ada cukup data untuk training dummy\n",
"\n",
" data_labels = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0] * 10 # 1 = Positif, 0 = Negatif\n",
"\n",
" # 2. Inisialisasi Project\n",
" analyzer = SentimentAnalyzer()\n",
"\n",
" # 3. Siapkan Data\n",
" analyzer.prepare_data(data_texts, data_labels)\n",
"\n",
" # 4. Latih Model (Fine-Tuning)\n",
" analyzer.train(epochs=3)\n",
"\n",
" # 5. Evaluasi Akhir\n",
" eval_results = analyzer.trainer.evaluate()\n",
" print(f\"\\n📊 Final Evaluation Results: {eval_results}\")\n",
"\n",
" # 6. Test Prediksi Manual\n",
" print(\"\\n--- 🔍 LIVE TEST PREDICTION ---\")\n",
" test_sentences = [\n",
" \"Aplikasi ini sangat membantu pekerjaan saya sehari-hari.\",\n",
" \"Sistemnya lemot parah, tolong diperbaiki.\"\n",
" ]\n",
"\n",
" for sentence in test_sentences:\n",
" sentiment = analyzer.predict(sentence)\n",
" print(f\"Input: '{sentence}'\\nPrediksi: {sentiment}\\n\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "MD6NM8w_fpTX",
"outputId": "5943baeb-6ac2-41c0-f019-e28cd6970cb6"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"🔄 System initialized using: cpu (Professional Mode)\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Data preparation complete.\n",
"🚀 Starting training process...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.12/dist-packages/notebook/notebookapp.py:191: SyntaxWarning: invalid escape sequence '\\/'\n",
" | |_| | '_ \\/ _` / _` | _/ -_)\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (1) Create a W&B account\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (2) Use an existing W&B account\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (3) Don't visualize my results\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Enter your choice:"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: You chose 'Use an existing W&B account'\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Logging into https://api.wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Find your API key here: https://wandb.ai/authorize?ref=models\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter:"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" ··········\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Invalid API key: API key must have 40+ characters, has 2.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (1) Create a W&B account\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (2) Use an existing W&B account\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (3) Don't visualize my results\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Enter your choice:"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: You chose \"Don't visualize my results\"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": []
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"Tracking run with wandb version 0.23.1"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"W&B syncing is set to <code>`offline`<code> in this directory. Run <code>`wandb online`<code> or set <code>WANDB_MODE=online<code> to enable cloud syncing.<br>Run data is saved locally in <code>/content/wandb/offline-run-20251225_172040-x70f2emp</code>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Detected [openai] in use.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: For more information, check out the docs at: https://weave-docs.wandb.ai/\n",
"/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
" warnings.warn(warn_msg)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [30/30 01:04, Epoch 3/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Accuracy</th>\n",
" <th>F1</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.669300</td>\n",
" <td>0.504732</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.337300</td>\n",
" <td>0.114364</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.082900</td>\n",
" <td>0.049298</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
" warnings.warn(warn_msg)\n",
"/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
" warnings.warn(warn_msg)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Training finished.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.12/dist-packages/torch/utils/data/dataloader.py:668: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n",
" warnings.warn(warn_msg)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [2/2 00:00]\n",
" </div>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"📊 Final Evaluation Results: {'eval_loss': 0.0492977574467659, 'eval_accuracy': 1.0, 'eval_f1': 1.0, 'eval_precision': 1.0, 'eval_recall': 1.0, 'eval_runtime': 1.0008, 'eval_samples_per_second': 19.983, 'eval_steps_per_second': 1.998, 'epoch': 3.0}\n",
"\n",
"--- 🔍 LIVE TEST PREDICTION ---\n",
"Input: 'Aplikasi ini sangat membantu pekerjaan saya sehari-hari.'\n",
"Prediksi: POSITIF 😊\n",
"\n",
"Input: 'Sistemnya lemot parah, tolong diperbaiki.'\n",
"Prediksi: NEGATIF 😠\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"MENGGUNAKAN GPU T4\n",
"USING GPU T4"
],
"metadata": {
"id": "j-0h6ynHCh0x"
}
},
{
"cell_type": "code",
"source": [
"!pip install transformers datasets evaluate torch scikit-learn matplotlib seaborn -q"
],
"metadata": {
"id": "M42upbatixKC"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from datasets import load_dataset\n",
"from transformers import (\n",
" DistilBertTokenizer,\n",
" DistilBertForSequenceClassification,\n",
" Trainer,\n",
" TrainingArguments,\n",
" DataCollatorWithPadding,\n",
" EarlyStoppingCallback\n",
")\n",
"from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix\n",
"\n",
"# Cek GPU\n",
"device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
"print(f\"🚀 Hardware Accelerator: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU (Slow)'}\")\n",
"\n",
"class DataPipeline:\n",
" def __init__(self, model_checkpoint='cahya/distilbert-base-indonesian'):\n",
" self.tokenizer = DistilBertTokenizer.from_pretrained(model_checkpoint)\n",
"\n",
" def load_and_process(self):\n",
" print(\"📥 Downloading IndoNLU Dataset via Raw URLs...\")\n",
"\n",
" data_files = {\n",
" \"train\": \"https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/smsa_doc-sentiment-prosa/train_preprocess.tsv\",\n",
" \"validation\": \"https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/smsa_doc-sentiment-prosa/valid_preprocess.tsv\"\n",
" }\n",
"\n",
" dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\", column_names=[\"text\", \"label\"])\n",
" self.label_map = {'positive': 2, 'neutral': 1, 'negative': 0}\n",
"\n",
" def process_function(examples):\n",
" tokenized = self.tokenizer(examples['text'], truncation=True, max_length=128)\n",
" tokenized['label'] = [self.label_map[l] for l in examples['label']]\n",
" return tokenized\n",
"\n",
" print(\"⚙️ Preprocessing & Tokenizing data...\")\n",
" tokenized_datasets = dataset.map(process_function, batched=True)\n",
" tokenized_datasets = tokenized_datasets.remove_columns(['text'])\n",
" tokenized_datasets.set_format(\"torch\")\n",
"\n",
" return tokenized_datasets[\"train\"], tokenized_datasets[\"validation\"]\n",
"\n",
"class AdvancedSentimentAnalyzer:\n",
" def __init__(self, model_checkpoint='cahya/distilbert-base-indonesian'):\n",
" self.device = device\n",
" self.model_checkpoint = model_checkpoint\n",
" self.model = DistilBertForSequenceClassification.from_pretrained(\n",
" model_checkpoint,\n",
" num_labels=3,\n",
" id2label={0: 'NEGATIF', 1: 'NETRAL', 2: 'POSITIF'},\n",
" label2id={'NEGATIF': 0, 'NETRAL': 1, 'POSITIF': 2}\n",
" ).to(self.device)\n",
" self.tokenizer = DistilBertTokenizer.from_pretrained(model_checkpoint)\n",
"\n",
" def compute_metrics(self, eval_pred):\n",
" # Kita cek apakah outputnya Tuple? Jika ya, ambil elemen pertama [0]\n",
" predictions = eval_pred.predictions\n",
" if isinstance(predictions, tuple):\n",
" predictions = predictions[0]\n",
"\n",
" labels = eval_pred.label_ids\n",
" # Ambil index dengan nilai tertinggi (argmax)\n",
" pred_ids = np.argmax(predictions, axis=-1)\n",
"\n",
" precision, recall, f1, _ = precision_recall_fscore_support(labels, pred_ids, average='weighted', zero_division=0)\n",
" acc = accuracy_score(labels, pred_ids)\n",
" return {'accuracy': acc, 'f1': f1, 'precision': precision, 'recall': recall}\n",
"\n",
" def train(self, train_data, val_data):\n",
" args = TrainingArguments(\n",
" output_dir=\"./sentimen_pro_results\",\n",
" eval_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" learning_rate=2e-5,\n",
" per_device_train_batch_size=32, # Batch besar karena GPU T4 kuat\n",
" per_device_eval_batch_size=32,\n",
" num_train_epochs=3,\n",
" weight_decay=0.01,\n",
" fp16=True, # Gunakan Mixed Precision (Cepat), bisa True atau torch.cuda.is_available()\n",
" load_best_model_at_end=True,\n",
" logging_dir='./logs',\n",
" logging_steps=50,\n",
" report_to=\"none\"\n",
" )\n",
"\n",
" data_collator = DataCollatorWithPadding(tokenizer=self.tokenizer)\n",
"\n",
" self.trainer = Trainer(\n",
" model=self.model,\n",
" args=args,\n",
" train_dataset=train_data,\n",
" eval_dataset=val_data,\n",
" tokenizer=self.tokenizer,\n",
" data_collator=data_collator,\n",
" compute_metrics=self.compute_metrics,\n",
" callbacks=[EarlyStoppingCallback(early_stopping_patience=1)]\n",
"\n",
" )\n",
"\n",
" print(\"\\n🔥 Starting Training on Tesla T4...\")\n",
" train_result = self.trainer.train()\n",
" print(f\"✅ Training Selesai. Runtime: {train_result.metrics['train_runtime']:.2f} detik\")\n",
" return train_result\n",
"\n",
" def visualize_performance(self, val_dataset):\n",
" print(\"\\n📊 Generating Visualizations...\")\n",
" predictions = self.trainer.predict(val_dataset)\n",
"\n",
" # FIX JUGA DI SINI (Untuk Visualisasi)\n",
" raw_preds = predictions.predictions\n",
" if isinstance(raw_preds, tuple):\n",
" raw_preds = raw_preds[0]\n",
"\n",
" preds = np.argmax(raw_preds, axis=-1)\n",
" labels = predictions.label_ids\n",
"\n",
" cm = confusion_matrix(labels, preds)\n",
" plt.figure(figsize=(8, 6))\n",
" sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
" xticklabels=['Negatif', 'Netral', 'Positif'],\n",
" yticklabels=['Negatif', 'Netral', 'Positif'])\n",
" plt.xlabel('Prediksi AI')\n",
" plt.ylabel('Fakta (Label Asli)')\n",
" plt.title('Confusion Matrix')\n",
" plt.show()\n",
"\n",
" def predict_live(self, text):\n",
" inputs = self.tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=128).to(self.device)\n",
" with torch.no_grad():\n",
" logits = self.model(**inputs).logits\n",
" predicted_class_id = logits.argmax().item()\n",
" return self.model.config.id2label[predicted_class_id]\n",
"\n",
"if __name__ == \"__main__\":\n",
" # 1. Pipeline Data\n",
" pipeline = DataPipeline()\n",
" train_data, val_data = pipeline.load_and_process()\n",
"\n",
" # 2. Setup Model\n",
" analyzer = AdvancedSentimentAnalyzer()\n",
"\n",
" # 3. Training Loop\n",
" analyzer.train(train_data, val_data)\n",
"\n",
" # 4. Evaluasi Visual\n",
" analyzer.visualize_performance(val_data)\n",
"\n",
" # 5. Live Test\n",
" print(\"\\n--- 🤖 LIVE DEMO ---\")\n",
" test_cases = [\n",
" \"Aplikasi ini sangat lemot, sering force close, nyesel download.\",\n",
" \"Barangnya biasa aja sih, sesuai harga, pengiriman standar.\",\n",
" \"Luar biasa! Fiturnya canggih dan sangat membantu pekerjaan saya.\",\n",
" \"Adminnya ramah banget tapi sayang aplikasinya masih banyak bug.\"\n",
" ]\n",
"\n",
" for text in test_cases:\n",
" pred = analyzer.predict_live(text)\n",
" color = \"🔴\" if pred == 'NEGATIF' else \"🟡\" if pred == 'NETRAL' else \"🟢\"\n",
" print(f\"Input: {text[:40]}... | Prediksi: {color} {pred}\")"
],
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},
"id": "MqsRjIzPi9qw",
"outputId": "5c7b6e10-2367-431f-eff4-6ab18abd634a"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h🚀 Hardware Accelerator: Tesla T4\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.12/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"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/62.0 [00:00<?, ?B/s]"
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"vocab.txt: 0.00B [00:00, ?B/s]"
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"📥 Downloading IndoNLU Dataset via Raw URLs...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading data: 0%| | 0.00/2.19M [00:00<?, ?B/s]"
],
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{
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"data": {
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0 examples [00:00, ? examples/s]"
],
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"version_major": 2,
"version_minor": 0,
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating validation split: 0 examples [00:00, ? examples/s]"
],
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"model_id": "16666bc662124681b33fba26a85c832a"
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},
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"⚙️ Preprocessing & Tokenizing data...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/11000 [00:00<?, ? examples/s]"
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{
"output_type": "display_data",
"data": {
"text/plain": [
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at cahya/distilbert-base-indonesian and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"/tmp/ipython-input-213376746.py:107: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
" self.trainer = Trainer(\n",
"The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"🔥 Starting Training on Tesla T4...\n"
]
},
{
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"data": {
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],
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"\n",
" <div>\n",
" \n",
" <progress value='688' max='1032' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [ 688/1032 01:50 < 00:55, 6.20 it/s, Epoch 2/3]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Accuracy</th>\n",
" <th>F1</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.225900</td>\n",
" <td>0.202893</td>\n",
" <td>0.926190</td>\n",
" <td>0.925238</td>\n",
" <td>0.926132</td>\n",
" <td>0.926190</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.149900</td>\n",
" <td>0.213086</td>\n",
" <td>0.928571</td>\n",
" <td>0.928019</td>\n",
" <td>0.928472</td>\n",
" <td>0.928571</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Training Selesai. Runtime: 112.73 detik\n",
"\n",
"📊 Generating Visualizations...\n"
]
},
{
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],
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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"--- 🤖 LIVE DEMO ---\n",
"Input: Aplikasi ini sangat lemot, sering force ... | Prediksi: 🔴 NEGATIF\n",
"Input: Barangnya biasa aja sih, sesuai harga, p... | Prediksi: 🔴 NEGATIF\n",
"Input: Luar biasa! Fiturnya canggih dan sangat ... | Prediksi: 🟢 POSITIF\n",
"Input: Adminnya ramah banget tapi sayang aplika... | Prediksi: 🟢 POSITIF\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch.nn.functional as F\n",
"\n",
"def predict_with_confidence(text):\n",
" # 1. Tokenisasi\n",
" inputs = analyzer.tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=128).to(analyzer.device)\n",
"\n",
" with torch.no_grad():\n",
" logits = analyzer.model(**inputs).logits\n",
"\n",
" # 2. Hitung Probabilitas (Softmax)\n",
" probs = F.softmax(logits, dim=-1)[0].cpu().numpy()\n",
"\n",
" # 3. Ambil Label\n",
" labels = ['NEGATIF', 'NETRAL ', 'POSITIF']\n",
" pred_idx = probs.argmax()\n",
"\n",
" # 4. Tampilkan Hasil Professional\n",
" print(f\"\\n📝 Input: '{text}'\")\n",
" print(\"-\" * 40)\n",
" for i, label in enumerate(labels):\n",
" bar_len = int(probs[i] * 20) # Visualisasi bar chart sederhana\n",
" bar = \"█\" * bar_len\n",
" print(f\"{label} : {bar:<20} {probs[i]*100:.2f}%\")\n",
"\n",
" final_pred = labels[pred_idx].strip()\n",
" color = \"🔴\" if final_pred == 'NEGATIF' else \"🟡\" if final_pred == 'NETRAL' else \"🟢\"\n",
" print(f\"👉 KEPUTUSAN FINAL: {color} {final_pred}\")\n",
"\n",
"# --- TES KHUSUS UNTUK MEMANCING 'NETRAL' ---\n",
"print(\"\\n--- 🔍 DEEP DIVE ANALYSIS ---\")\n",
"\n",
"neutral_triggers = [\n",
" \"Pesanan sudah sampai di rumah.\", # Fakta murni (Harusnya Netral/Positif)\n",
" \"Barang sesuai pesanan.\", # Fakta (Cenderung Netral/Positif)\n",
" \"Warna baju ini merah.\", # Fakta Objektif (Sangat Netral)\n",
" \"Tidak ada komentar.\", # Netral murni\n",
" \"Lumayan lah buat harga segitu.\", # Ambigu (Cek apakah masuk Netral/Negatif)\n",
" \"Barangnya biasa aja sih, sesuai harga, pengiriman standar.\" # Kalimat Anda tadi\n",
"]\n",
"\n",
"for text in neutral_triggers:\n",
" predict_with_confidence(text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CXWRDyWQ9Nkb",
"outputId": "22ff2ab7-50fb-4fa3-b8fd-53bb11de3765"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"--- 🔍 DEEP DIVE ANALYSIS ---\n",
"\n",
"📝 Input: 'Pesanan sudah sampai di rumah.'\n",
"----------------------------------------\n",
"NEGATIF : ████ 20.23%\n",
"NETRAL : ██████████████ 74.25%\n",
"POSITIF : █ 5.53%\n",
"👉 KEPUTUSAN FINAL: 🟡 NETRAL\n",
"\n",
"📝 Input: 'Barang sesuai pesanan.'\n",
"----------------------------------------\n",
"NEGATIF : ██ 13.86%\n",
"NETRAL : █████ 26.53%\n",
"POSITIF : ███████████ 59.61%\n",
"👉 KEPUTUSAN FINAL: 🟢 POSITIF\n",
"\n",
"📝 Input: 'Warna baju ini merah.'\n",
"----------------------------------------\n",
"NEGATIF : ███ 15.83%\n",
"NETRAL : ██████ 32.35%\n",
"POSITIF : ██████████ 51.82%\n",
"👉 KEPUTUSAN FINAL: 🟢 POSITIF\n",
"\n",
"📝 Input: 'Tidak ada komentar.'\n",
"----------------------------------------\n",
"NEGATIF : ████████ 43.74%\n",
"NETRAL : ████ 20.52%\n",
"POSITIF : ███████ 35.74%\n",
"👉 KEPUTUSAN FINAL: 🔴 NEGATIF\n",
"\n",
"📝 Input: 'Lumayan lah buat harga segitu.'\n",
"----------------------------------------\n",
"NEGATIF : 3.73%\n",
"NETRAL : 0.43%\n",
"POSITIF : ███████████████████ 95.84%\n",
"👉 KEPUTUSAN FINAL: 🟢 POSITIF\n",
"\n",
"📝 Input: 'Barangnya biasa aja sih, sesuai harga, pengiriman standar.'\n",
"----------------------------------------\n",
"NEGATIF : ██████████████████ 92.87%\n",
"NETRAL : 2.42%\n",
"POSITIF : 4.71%\n",
"👉 KEPUTUSAN FINAL: 🔴 NEGATIF\n"
]
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