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+{
+ "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": 1,
+   "id": "53a214ae-c9cf-4d46-925d-068f1685537b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "All arrays must be of the same length",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 28\u001b[39m\n\u001b[32m     11\u001b[39m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[32m     12\u001b[39m \u001b[38;5;66;03m# 1. Contoh Dataset\u001b[39;00m\n\u001b[32m     13\u001b[39m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[32m     14\u001b[39m \u001b[38;5;66;03m# Anda bisa mengganti dataset ini dengan dataset lain (CSV, JSON, dll)\u001b[39;00m\n\u001b[32m     16\u001b[39m data = {\n\u001b[32m     17\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mtext\u001b[39m\u001b[33m\"\u001b[39m: [\n\u001b[32m     18\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mTempat ini sangat nyaman dan bersih.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   (...)\u001b[39m\u001b[32m     25\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mlabel\u001b[39m\u001b[33m\"\u001b[39m: [\u001b[33m\"\u001b[39m\u001b[33mpositive\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mnegative\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpositive\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mnegative\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpositive\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mnegative\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     26\u001b[39m }\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     30\u001b[39m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[32m     31\u001b[39m \u001b[38;5;66;03m# 2. Split Train & Test\u001b[39;00m\n\u001b[32m     32\u001b[39m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[32m     33\u001b[39m X_train, X_test, y_train, y_test = train_test_split(\n\u001b[32m     34\u001b[39m     df[\u001b[33m\"\u001b[39m\u001b[33mtext\u001b[39m\u001b[33m\"\u001b[39m], df[\u001b[33m\"\u001b[39m\u001b[33mlabel\u001b[39m\u001b[33m\"\u001b[39m], test_size=\u001b[32m0.3\u001b[39m, random_state=\u001b[32m42\u001b[39m\n\u001b[32m     35\u001b[39m )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniforge3\\Lib\\site-packages\\pandas\\core\\frame.py:782\u001b[39m, in \u001b[36mDataFrame.__init__\u001b[39m\u001b[34m(self, data, index, columns, dtype, copy)\u001b[39m\n\u001b[32m    776\u001b[39m     mgr = \u001b[38;5;28mself\u001b[39m._init_mgr(\n\u001b[32m    777\u001b[39m         data, axes={\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m: index, \u001b[33m\"\u001b[39m\u001b[33mcolumns\u001b[39m\u001b[33m\"\u001b[39m: columns}, dtype=dtype, copy=copy\n\u001b[32m    778\u001b[39m     )\n\u001b[32m    780\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[32m    781\u001b[39m     \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m782\u001b[39m     mgr = \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    783\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma.MaskedArray):\n\u001b[32m    784\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mma\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m mrecords\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniforge3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:503\u001b[39m, in \u001b[36mdict_to_mgr\u001b[39m\u001b[34m(data, index, columns, dtype, typ, copy)\u001b[39m\n\u001b[32m    499\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    500\u001b[39m         \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[32m    501\u001b[39m         arrays = [x.copy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[33m\"\u001b[39m\u001b[33mdtype\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[32m--> \u001b[39m\u001b[32m503\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniforge3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:114\u001b[39m, in \u001b[36marrays_to_mgr\u001b[39m\u001b[34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[39m\n\u001b[32m    111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[32m    112\u001b[39m     \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[32m    113\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m114\u001b[39m         index = \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    115\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    116\u001b[39m         index = ensure_index(index)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~\\miniforge3\\Lib\\site-packages\\pandas\\core\\internals\\construction.py:677\u001b[39m, in \u001b[36m_extract_index\u001b[39m\u001b[34m(data)\u001b[39m\n\u001b[32m    675\u001b[39m lengths = \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(raw_lengths))\n\u001b[32m    676\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lengths) > \u001b[32m1\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m677\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mAll arrays must be of the same length\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m    679\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m have_dicts:\n\u001b[32m    680\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    681\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mMixing dicts with non-Series may lead to ambiguous ordering.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    682\u001b[39m     )\n",
+      "\u001b[31mValueError\u001b[39m: All arrays must be of the same length"
+     ]
+    }
+   ],
+   "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",
+    "        \"Tempat ini sangat nyaman dan bersih.\"\n",
+    "        \"Akses menuju ke sana cukup sulit dan membingungkan.\"\n",
+    "        \"Pelayanan staf di sini juga sangat ramah dan cepat tanggap.\"\n",
+    "        \"Lokasi kafe ini strategis dan mudah ditemukan.\"\n",
+    "        \"Suasananya kadang terlalu bising karena sering ada keramaian.\"\n",
+    "        \"Pilihan menu minumannya sangat beragam dan lezat.\"\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 = [\"Tempat nyaman, saya suka\"]\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": [
+    "#sample_text = [\"barang bagus luar biasa\"]\n",
+    "sample_text = [\"Tempat bising saya tidak suka\"]\n",
+    "sample_vec = tfidf.transform(sample_text)\n",
+    "prediction = model.predict(sample_vec)\n",
+    "print(\"\\nPrediksi untuk:\", sample_text[0])\n",
+    "print(\"Hasil:\", prediction[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0413b4bf-beb1-483b-a081-b540fce1b21c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d714bd96-09a0-4439-8286-0cb39e2fb4df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+}