{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4a1399a-f23d-4060-a07e-bce5a5c7ddac",
   "metadata": {},
   "source": [
    "# Klasifikasi Teks menggunakan ANN\n",
    "## Arif R Dwiyanto\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53a214ae-c9cf-4d46-925d-068f1685537b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Classification Report ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.00      0.00      0.00       1.0\n",
      "    positive       0.00      0.00      0.00       1.0\n",
      "\n",
      "    accuracy                           0.00       2.0\n",
      "   macro avg       0.00      0.00      0.00       2.0\n",
      "weighted avg       0.00      0.00      0.00       2.0\n",
      "\n",
      "=== Confusion Matrix ===\n",
      "[[0 1]\n",
      " [1 0]]\n",
      "\n",
      "Prediksi untuk: barang buruk, saya kecewa\n",
      "Hasil: negative\n"
     ]
    }
   ],
   "source": [
    "# ---------------------------------------------------------\n",
    "# Klasifikasi Teks dengan TF-IDF + Feedforward Neural Network\n",
    "# ---------------------------------------------------------\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "# -----------------------------------------\n",
    "# 1. Contoh Dataset\n",
    "# -----------------------------------------\n",
    "# Anda bisa mengganti dataset ini dengan dataset lain (CSV, JSON, dll)\n",
    "\n",
    "data = {\n",
    "    \"text\": [\n",
    "        \"Saya suka produk ini, luar biasa\",\n",
    "        \"Layanannya buruk, sangat kecewa\",\n",
    "        \"Pembelian terbaik yang pernah saya lakukan\",\n",
    "        \"Saya benci produk ini, buang-buang uang\",\n",
    "        \"Kualitasnya sangat bagus, direkomendasikan\",\n",
    "        \"Pengalaman buruk, tidak akan membeli lagi\"\n",
    "    ],\n",
    "    \"label\": [\"positive\", \"negative\", \"positive\", \"negative\", \"positive\", \"negative\"]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# -----------------------------------------\n",
    "# 2. Split Train & Test\n",
    "# -----------------------------------------\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df[\"text\"], df[\"label\"], test_size=0.3, random_state=42\n",
    ")\n",
    "\n",
    "# -----------------------------------------\n",
    "# 3. TF-IDF Vectorization\n",
    "# -----------------------------------------\n",
    "tfidf = TfidfVectorizer(max_features=5000)\n",
    "X_train_tfidf = tfidf.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf.transform(X_test)\n",
    "\n",
    "# -----------------------------------------\n",
    "# 4. Feedforward ANN (MLPClassifier)\n",
    "# -----------------------------------------\n",
    "model = MLPClassifier(\n",
    "    hidden_layer_sizes=(256, 64),\n",
    "    activation='relu',\n",
    "    solver='adam',\n",
    "    max_iter=500,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "model.fit(X_train_tfidf, y_train)\n",
    "\n",
    "# -----------------------------------------\n",
    "# 5. Evaluasi Model\n",
    "# -----------------------------------------\n",
    "y_pred = model.predict(X_test_tfidf)\n",
    "\n",
    "print(\"=== Classification Report ===\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(\"=== Confusion Matrix ===\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "# -----------------------------------------\n",
    "# 6. Prediksi Teks Baru\n",
    "# -----------------------------------------\n",
    "sample_text = [\"barang bagus luar biasa\"]\n",
    "sample_text = [\"barang buruk, saya kecewa\"]\n",
    "sample_vec = tfidf.transform(sample_text)\n",
    "prediction = model.predict(sample_vec)\n",
    "\n",
    "print(\"\\nPrediksi untuk:\", sample_text[0])\n",
    "print(\"Hasil:\", prediction[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f7d90fe-4af4-446c-9547-c9312bfa6fc7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}