diff --git a/NLP/.ipynb_checkpoints/Klasifikasi Teks FNN-checkpoint.ipynb b/NLP/.ipynb_checkpoints/Klasifikasi Teks FNN-checkpoint.ipynb
index 363fcab..1e6a25e 100644
--- a/NLP/.ipynb_checkpoints/Klasifikasi Teks FNN-checkpoint.ipynb	
+++ b/NLP/.ipynb_checkpoints/Klasifikasi Teks FNN-checkpoint.ipynb	
@@ -1,6 +1,151 @@
 {
- "cells": [],
- "metadata": {},
+ "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
 }
diff --git a/NLP/Klasifikasi Teks FNN.ipynb b/NLP/Klasifikasi Teks FNN.ipynb
index cae22a7..1e6a25e 100644
--- a/NLP/Klasifikasi Teks FNN.ipynb	
+++ b/NLP/Klasifikasi Teks FNN.ipynb	
@@ -5,8 +5,8 @@
    "id": "f4a1399a-f23d-4060-a07e-bce5a5c7ddac",
    "metadata": {},
    "source": [
-    "# Klasifikasi Teks\n",
-    "## Arif R Dwiyanto"
+    "# Klasifikasi Teks menggunakan ANN\n",
+    "## Arif R Dwiyanto\n"
    ]
   },
   {