diff --git "a/01_data_exploration.ipynb" "b/01_data_exploration.ipynb"
new file mode 100644--- /dev/null
+++ "b/01_data_exploration.ipynb"
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4fcd75be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DATA_DIR auto-detected: c:\\Users\\user\\Documents\\Project\\batik_vision_project\\Batik-Indonesia\n",
+      "Memindai folder data di: c:\\Users\\user\\Documents\\Project\\batik_vision_project\\Batik-Indonesia\n",
+      "Selesai. Ditemukan total 2599 file dalam 38 kelas.\n",
+      "\n",
+      "--- Statistik Distribusi Kelas ---\n",
+      "            count\n",
+      "count   38.000000\n",
+      "mean    68.394737\n",
+      "std     46.551651\n",
+      "min     22.000000\n",
+      "25%     43.250000\n",
+      "50%     50.000000\n",
+      "75%     80.000000\n",
+      "max    230.000000\n",
+      "\n",
+      "Membuat plot distribusi kelas...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_20344\\3633848228.py:68: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x=df['count'], y=df.index, palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from PIL import Image, UnidentifiedImageError\n",
+    "import random\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# --- Konfigurasi: cari folder data secara otomatis jika memungkinkan ---\n",
+    "# Nama-nama folder yang mungkin dibuat oleh skrip download atau struktur Anda\n",
+    "POSSIBLE_NAMES = [\"Batik_Indonesia_JPG\", \"Batik-Indonesia\", \"Batik_Indonesia\", \"data\", \"dataset\"]\n",
+    "\n",
+    "# Cek beberapa lokasi relatif (notebook biasanya berada di src/)\n",
+    "search_roots = [Path.cwd(), Path.cwd().parent]\n",
+    "candidates = []\n",
+    "for root in search_roots:\n",
+    "    for name in POSSIBLE_NAMES:\n",
+    "        p = root / name\n",
+    "        if p.exists() and p.is_dir():\n",
+    "            candidates.append(p)\n",
+    "\n",
+    "# Jika belum ketemu, coba cari folder yang mengandung 'Batik' di parent\n",
+    "if not candidates:\n",
+    "    for d in Path.cwd().parent.glob(\"*Batik*\"):\n",
+    "        if d.is_dir():\n",
+    "            candidates.append(d)\n",
+    "\n",
+    "if candidates:\n",
+    "    DATA_DIR = str(candidates[0])\n",
+    "    print(f\"DATA_DIR auto-detected: {DATA_DIR}\")\n",
+    "else:\n",
+    "    raise FileNotFoundError(\n",
+    "        \"Tidak menemukan folder data. Coba: 1) jalankan download_script.py agar menghasilkan 'Batik_Indonesia_JPG', \"\n",
+    "        \"atau 2) set DATA_DIR manual ke path folder gambar. Dicari di: \" + \", \".join(str(p) for p in search_roots)\n",
+    "    )\n",
+    "\n",
+    "# --- 1. Memindai Data ---\n",
+    "print(f\"Memindai folder data di: {DATA_DIR}\")\n",
+    "\n",
+    "class_counts = {}\n",
+    "total_files = 0\n",
+    "\n",
+    "# Loop melalui setiap folder kelas di dalam DATA_DIR\n",
+    "for class_name in os.listdir(DATA_DIR):\n",
+    "    class_path = os.path.join(DATA_DIR, class_name)\n",
+    "    \n",
+    "    # Pastikan itu adalah folder\n",
+    "    if os.path.isdir(class_path):\n",
+    "        files = os.listdir(class_path)\n",
+    "        class_counts[class_name] = len(files)\n",
+    "        total_files += len(files)\n",
+    "\n",
+    "print(f\"Selesai. Ditemukan total {total_files} file dalam {len(class_counts)} kelas.\")\n",
+    "\n",
+    "# --- 2. Membuat DataFrame untuk Analisis ---\n",
+    "# Mengubah kamus menjadi DataFrame Pandas agar mudah dianalisis\n",
+    "df = pd.DataFrame.from_dict(class_counts, orient='index', columns=['count'])\n",
+    "df = df.sort_values(by='count', ascending=False)\n",
+    "\n",
+    "# --- 3. Menampilkan Statistik Dasar ---\n",
+    "print(\"\\n--- Statistik Distribusi Kelas ---\")\n",
+    "print(df.describe())\n",
+    "\n",
+    "# --- 4. Visualisasi Distribusi Kelas ---\n",
+    "print(\"\\nMembuat plot distribusi kelas...\")\n",
+    "\n",
+    "plt.figure(figsize=(12, 10)) # Ukuran plot\n",
+    "sns.barplot(x=df['count'], y=df.index, palette='viridis')\n",
+    "plt.title(f'Distribusi {len(class_counts)} Kelas Batik (Total {total_files} Gambar)', fontsize=16)\n",
+    "plt.xlabel('Jumlah Gambar', fontsize=12)\n",
+    "plt.ylabel('Nama Kelas', fontsize=12)\n",
+    "plt.tight_layout() # Merapikan plot\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}