sista_mif_ta/data/prepare_corpus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73b2b3ae",
   "metadata": {},
   "source": [
    "# Persiapan Corpus — SISTA MIF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd472692",
   "metadata": {},
   "source": [
    "## 1. Import Library & Konfigurasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ed4261a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Konfigurasi selesai.\n",
      "   File input : d:\\Project\\sista_mif_ta\\data\\ts_internal_mif.xlsx\n",
      "   Output dir : d:\\Project\\sista_mif_ta\\data\\processed\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from pathlib import Path\n",
    "from sklearn.model_selection import train_test_split\n",
    "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "factory = StemmerFactory()\n",
    "stemmer = factory.create_stemmer()\n",
    "\n",
    "BASE_DIR   = Path.cwd()\n",
    "DATA_DIR   = BASE_DIR\n",
    "OUTPUT_DIR = DATA_DIR / \"processed\"\n",
    "OUTPUT_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "FILE_INTERNAL = DATA_DIR / \"ts_internal_mif.xlsx\"\n",
    "SEPARATOR     = \";\"\n",
    "ENCODING      = \"utf-8-sig\"\n",
    "TARGET_CLASSES = [\"Programmer\", \"Data Analyst\", \"Wirausaha Informatika\", \"Non-IT\"]\n",
    "\n",
    "KLASIFIKASI_MAP = {\n",
    "    \"Programmer\": \"Programmer\",\n",
    "    \"Data Analyst\": \"Data Analyst\",\n",
    "    \"Wirausaha IT\": \"Wirausaha Informatika\",\n",
    "    \"Wirausaha\": \"Wirausaha Informatika\",\n",
    "    \"Non-IT\": \"Non-IT\",\n",
    "    \"Infokom\": None, \"Pelajar\": None, \"Tidak Bekerja\": None, \"TIdak diketahui\": None\n",
    "}\n",
    "KEYWORD_RULES = {\n",
    "    \"Programmer\": [\"programmer\", \"developer\", \"engineer\", \"fullstack\", \"backend\", \"frontend\", \"mobile\", \"android\", \"ios\", \"software\", \"web dev\", \"coding\", \"it staff\", \"teknisi\", \"sistem informasi\", \"application\", \"network\", \"devops\", \"qa\", \"tester\", \"ui\", \"ux\", \"swe\"],\n",
    "    \"Data Analyst\": [\"data analyst\", \"analis data\", \"data science\", \"business analyst\", \"research\", \"statistik\", \"bi analyst\", \"reporting\", \"database\", \"sql\", \"etl\", \"data engineer\", \"big data\", \"analyst\", \"data mining\", \"machine learning\", \"data visual\", \"power bi\", \"tableau\", \"looker\", \"business intelligence\", \"bi developer\", \"data warehouse\"],\n",
    "    \"Wirausaha Informatika\": [\"founder\", \"owner\", \"ceo\", \"wiraswasta\", \"startup\", \"freelance\", \"freelancer\", \"wirausaha\", \"bisnis\", \"usaha mandiri\", \"konsultan\", \"co founder\", \"entrepreneur\", \"self employed\", \"owner toko\", \"usaha\", \"dagang online\", \"tokopedia\", \"shopee\", \"dropship\", \"reseller\"]\n",
    "}\n",
    "COMPANY_STOPWORDS = {\n",
    "    \"pt\", \"cv\", \"ud\", \"tbk\", \"persero\", \"corp\", \"inc\", \"ltd\", \"koperasi\", \"bumn\", \"bumd\",\n",
    "    \"dinas\", \"kantor\", \"pemkab\", \"pemprov\", \"politeknik\", \"universitas\", \"sekolah\", \"sma\", \"smk\", \"sd\",\n",
    "    \"bank\", \"bpr\", \"rs\", \"rumah sakit\", \"klinik\", \"apotek\", \"hotel\", \"restoran\", \"cafe\", \"toko\", \"konter\",\n",
    "    \"foundation\", \"yayasan\", \"perkumpulan\", \"organisasi\", \"agency\", \"studio\", \"consulting\", \"group\", \"holding\"\n",
    "}\n",
    "\n",
    "print(\"Konfigurasi selesai.\")\n",
    "print(f\"   File input : {FILE_INTERNAL}\")\n",
    "print(f\"   Output dir : {OUTPUT_DIR}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c9c79a",
   "metadata": {},
   "source": [
    "## 2. Definisi Fungsi Bantu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f880576a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semua fungsi bantu berhasil didefinisikan.\n"
     ]
    }
   ],
   "source": [
    "def load_data_file(file_path: str) -> pd.DataFrame:\n",
    "    ext = Path(file_path).suffix.lower()\n",
    "    if ext == '.csv':\n",
    "        try:\n",
    "            return pd.read_csv(file_path, sep=SEPARATOR, encoding=ENCODING, dtype=str, on_bad_lines='skip', engine='python')\n",
    "        except UnicodeDecodeError:\n",
    "            return pd.read_csv(file_path, sep=SEPARATOR, encoding='latin1', dtype=str, on_bad_lines='skip', engine='python')\n",
    "    elif ext == '.xlsx':\n",
    "        return pd.read_excel(file_path, dtype=str)\n",
    "    else:\n",
    "        raise ValueError(f\"Format tidak didukung: {ext}\")\n",
    "\n",
    "def find_col(df: pd.DataFrame, keywords: list) -> str | None:\n",
    "    for col in df.columns:\n",
    "        col_clean = str(col).strip().lower()\n",
    "        if any(k.strip().lower() in col_clean for k in keywords):\n",
    "            return col\n",
    "    return None\n",
    "\n",
    "def clean_text(text: str) -> str:\n",
    "    if pd.isna(text) or str(text).strip().lower() in [\"nan\", \"none\", \"null\", \"-\", \"0\", \"\", \"tidak diisi\", \"tidak diketahui\"]:\n",
    "        return \"\"\n",
    "    text = str(text).strip().lower()\n",
    "    text = re.sub(r'^\\d+\\s*[-:/]\\s*', '', text)\n",
    "    text = text.replace('-', ' ').replace('/', ' ').replace('_', ' ').replace(',', ' ')\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)\n",
    "    text = re.sub(r'\\d+', '', text)\n",
    "    tokens = [w for w in text.split() if w not in COMPANY_STOPWORDS and len(w) >= 3]\n",
    "    return \" \".join([stemmer.stem(w) for w in tokens])\n",
    "\n",
    "def is_likely_name(text: str, full_name: str) -> bool:\n",
    "    if not text or not full_name: return False\n",
    "    text_clean = re.sub(r'[^\\w\\s]', '', text.lower())\n",
    "    name_clean = re.sub(r'[^\\w\\s]', '', str(full_name).lower())\n",
    "    text_parts = set(text_clean.split())\n",
    "    name_parts = set(name_clean.split())\n",
    "    if len(text_parts) == 0: return False\n",
    "    if len(text_parts) <= 3 and text_parts.issubset(name_parts):\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def classify_rule_based(text: str) -> str:\n",
    "    if not text or len(text) < 3: return \"Non-IT\"\n",
    "    for profile in [\"Programmer\", \"Data Analyst\", \"Wirausaha Informatika\"]:\n",
    "        if any(kw in text for kw in KEYWORD_RULES[profile]): return profile\n",
    "    return \"Non-IT\"\n",
    "\n",
    "print(\"Semua fungsi bantu berhasil didefinisikan.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf96089",
   "metadata": {},
   "source": [
    "## 3. Memuat & Membersihkan Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa0d1e7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/4] Memuat & Membersihkan Data...\n",
      "   Kolom NIM         : nim\n",
      "   Kolom Nama        : nama_lengkap\n",
      "   Kolom Jabatan     : jabatan\n",
      "   Kolom Jabatan Baru: jabatan_terupdate\n",
      "   Kolom Klasifikasi : klasifikasi_pekerjaan\n",
      "   Kolom Status      : status_pekerjaan\n",
      "\n",
      "   Total baris dimuat: 488\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama_lengkap</th>\n",
       "      <th>nim</th>\n",
       "      <th>email</th>\n",
       "      <th>no_telepon</th>\n",
       "      <th>alamat_domisili</th>\n",
       "      <th>jurusan</th>\n",
       "      <th>program_studi</th>\n",
       "      <th>tahun_masuk</th>\n",
       "      <th>tahun_lulus</th>\n",
       "      <th>status_pekerjaan</th>\n",
       "      <th>...</th>\n",
       "      <th>jumlah_undangan_wawancara</th>\n",
       "      <th>masa_tunggu_pra_lulus</th>\n",
       "      <th>masa_tunggu_pasca_lulus</th>\n",
       "      <th>total_masa_tunggu</th>\n",
       "      <th>instansi_terupdate</th>\n",
       "      <th>jabatan_terupdate</th>\n",
       "      <th>tahun_bekerja</th>\n",
       "      <th>status_pekerjaan_terupdate</th>\n",
       "      <th>linkedin_profile</th>\n",
       "      <th>sosmed_ig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABRAHAM FIRDAUS FATHURROSI</td>\n",
       "      <td>E31170249</td>\n",
       "      <td>abrahamfirdaus86@gmail.com</td>\n",
       "      <td>081217394021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Teknologi Informasi</td>\n",
       "      <td>Manajemen Informatika</td>\n",
       "      <td>2016</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.linkedin.com/in/abraham-firdaus-fa...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACHMAD APRILIANDI ALALLAH</td>\n",
       "      <td>E31170384</td>\n",
       "      <td>andypunk64@gmail.com</td>\n",
       "      <td>082247877665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Teknologi Informasi</td>\n",
       "      <td>Manajemen Informatika</td>\n",
       "      <td>2016</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.linkedin.com/in/achmad-apriliandi-...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADHITYA PUTRA WARDHANA</td>\n",
       "      <td>E31151908</td>\n",
       "      <td>adhityaputra1997@gmail.com</td>\n",
       "      <td>081333682804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Teknologi Informasi</td>\n",
       "      <td>Manajemen Informatika</td>\n",
       "      <td>2016</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.linkedin.com/in/adhitya-putra-ward...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 nama_lengkap        nim                       email  \\\n",
       "0  ABRAHAM FIRDAUS FATHURROSI  E31170249  abrahamfirdaus86@gmail.com   \n",
       "1   ACHMAD APRILIANDI ALALLAH  E31170384        andypunk64@gmail.com   \n",
       "2      ADHITYA PUTRA WARDHANA  E31151908  adhityaputra1997@gmail.com   \n",
       "\n",
       "     no_telepon alamat_domisili              jurusan          program_studi  \\\n",
       "0  081217394021             NaN  Teknologi Informasi  Manajemen Informatika   \n",
       "1  082247877665             NaN  Teknologi Informasi  Manajemen Informatika   \n",
       "2  081333682804             NaN  Teknologi Informasi  Manajemen Informatika   \n",
       "\n",
       "  tahun_masuk tahun_lulus status_pekerjaan  ... jumlah_undangan_wawancara  \\\n",
       "0        2016        2019              NaN  ...                       NaN   \n",
       "1        2016        2019              NaN  ...                       NaN   \n",
       "2        2016        2019              NaN  ...                       NaN   \n",
       "\n",
       "  masa_tunggu_pra_lulus masa_tunggu_pasca_lulus total_masa_tunggu  \\\n",
       "0                   NaN                     NaN               NaN   \n",
       "1                   NaN                     NaN               NaN   \n",
       "2                   NaN                     NaN               NaN   \n",
       "\n",
       "  instansi_terupdate jabatan_terupdate tahun_bekerja  \\\n",
       "0                NaN               NaN           NaN   \n",
       "1                NaN               NaN           NaN   \n",
       "2                NaN               NaN           NaN   \n",
       "\n",
       "  status_pekerjaan_terupdate  \\\n",
       "0                        NaN   \n",
       "1                        NaN   \n",
       "2                        NaN   \n",
       "\n",
       "                                    linkedin_profile sosmed_ig  \n",
       "0  https://www.linkedin.com/in/abraham-firdaus-fa...       NaN  \n",
       "1  https://www.linkedin.com/in/achmad-apriliandi-...       NaN  \n",
       "2  https://www.linkedin.com/in/adhitya-putra-ward...       NaN  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert FILE_INTERNAL.exists(), f\"File tidak ditemukan: {FILE_INTERNAL}\"\n",
    "\n",
    "print(\"[1/4] Memuat & Membersihkan Data...\")\n",
    "df = load_data_file(str(FILE_INTERNAL))\n",
    "df.columns = df.columns.str.strip()\n",
    "\n",
    "col_nim         = find_col(df, [\"nim\"])\n",
    "col_nama        = find_col(df, [\"nama\", \"lengkap\"])\n",
    "col_jab_lama    = find_col(df, [\"jabatan\"])\n",
    "col_jab_baru    = find_col(df, [\"jabatan_terupdate\"])\n",
    "col_klasifikasi = find_col(df, [\"klasifikasi\"])\n",
    "col_status      = find_col(df, [\"status\", \"kerja\"])\n",
    "\n",
    "print(f\"   Kolom NIM         : {col_nim}\")\n",
    "print(f\"   Kolom Nama        : {col_nama}\")\n",
    "print(f\"   Kolom Jabatan     : {col_jab_lama}\")\n",
    "print(f\"   Kolom Jabatan Baru: {col_jab_baru}\")\n",
    "print(f\"   Kolom Klasifikasi : {col_klasifikasi}\")\n",
    "print(f\"   Kolom Status      : {col_status}\")\n",
    "print(f\"\\n   Total baris dimuat: {len(df)}\")\n",
    "\n",
    "assert all([col_nim, col_nama, col_jab_lama]), \"Kolom esensial tidak ditemukan!\"\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4d385d",
   "metadata": {},
   "source": [
    "## 4. Penggabungan Kolom Jabatan & Filter Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c1c8ef4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Kolom jabatan digabung: 448 baris fallback ke jabatan lama\n",
      "   Filter status kerja: 13 baris dihapus -> tersisa 475 baris\n"
     ]
    }
   ],
   "source": [
    "if col_jab_baru:\n",
    "    mask_empty = df[col_jab_baru].isna() | df[col_jab_baru].astype(str).str.strip().str.lower().isin(\n",
    "        [\"\", \"-\", \"0\", \"nan\", \"none\", \"null\", \"tidak diisi\", \"tidak diketahui\"]\n",
    "    )\n",
    "    df[\"jabatan_final\"] = df[col_jab_baru].where(~mask_empty, df[col_jab_lama])\n",
    "    print(f\"   Kolom jabatan digabung: {mask_empty.sum()} baris fallback ke jabatan lama\")\n",
    "else:\n",
    "    df[\"jabatan_final\"] = df[col_jab_lama]\n",
    "    print(\"   Menggunakan kolom jabatan lama (jabatan_terupdate tidak ditemukan)\")\n",
    "\n",
    "col_jabatan = \"jabatan_final\"\n",
    "\n",
    "before_filter = len(df)\n",
    "if col_status:\n",
    "    blacklist = [\"tidah diketahui\", \"tidak bekerja\", \"pelajar\", \"melanjutkan pendidikan\", \"nan\", \"\"]\n",
    "    mask = ~df[col_status].str.lower().str.strip().isin(blacklist)\n",
    "    df = df[mask].copy()\n",
    "    print(f\"   Filter status kerja: {before_filter - len(df)} baris dihapus -> tersisa {len(df)} baris\")\n",
    "else:\n",
    "    print(\"   Kolom status tidak ditemukan, tidak ada filter status.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65797b5c",
   "metadata": {},
   "source": [
    "## 5. Pembersihan Teks (Stemming & Stopword Removal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfc2abf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2/4] Menjalankan pembersihan teks (ini bisa memakan waktu beberapa menit)...\n",
      "   Selesai. Contoh hasil cleaning:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jabatan Asli</th>\n",
       "      <th>Setelah Cleaning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CUSTOMER SERVICE</td>\n",
       "      <td>customer service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Staff Administrasi Produksi</td>\n",
       "      <td>staff administrasi produksi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Customer Service Staff</td>\n",
       "      <td>customer service staff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Web Developer</td>\n",
       "      <td>web developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Human Resources Development Officer</td>\n",
       "      <td>human resources development officer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Jabatan Asli                     Setelah Cleaning\n",
       "1                      CUSTOMER SERVICE                     customer service\n",
       "5           Staff Administrasi Produksi          staff administrasi produksi\n",
       "8                Customer Service Staff               customer service staff\n",
       "9                         Web Developer                        web developer\n",
       "13  Human Resources Development Officer  human resources development officer"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"[2/4] Menjalankan pembersihan teks (ini bisa memakan waktu beberapa menit)...\")\n",
    "df[\"job_text_raw\"] = df[col_jabatan].apply(clean_text)\n",
    "print(f\"   Selesai. Contoh hasil cleaning:\")\n",
    "\n",
    "sample = df[[col_jabatan, \"job_text_raw\"]].dropna().head(5)\n",
    "sample.columns = [\"Jabatan Asli\", \"Setelah Cleaning\"]\n",
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377a080f",
   "metadata": {},
   "source": [
    "## 6. Fallback Teks & Deteksi Kebocoran Nama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ffe70c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Fallback teks dari kolom klasifikasi: 146 baris\n",
      "   Mendeteksi kebocoran nama pribadi...\n",
      "   Terdeteksi 0 baris dengan indikasi nama pribadi -> dihapus.\n"
     ]
    }
   ],
   "source": [
    "if col_klasifikasi:\n",
    "    fallback_map = {\n",
    "        \"Programmer\": \"programmer developer\", \"Data Analyst\": \"data analyst\",\n",
    "        \"Wirausaha IT\": \"wirausaha founder\", \"Wirausaha\": \"wirausaha founder\",\n",
    "        \"Non IT\": \"staff admin\", \"Infokom\": \"it staff teknisi\",\n",
    "        \"TIdah diketahui\": \"\", \"Pelajar\": \"\", \"Tidak Bekerja\": \"\"\n",
    "    }\n",
    "    empty_mask = df[\"job_text_raw\"] == \"\"\n",
    "    if empty_mask.any():\n",
    "        df.loc[empty_mask, \"job_text_raw\"] = df.loc[empty_mask, col_klasifikasi].map(fallback_map).fillna(\"\")\n",
    "        print(f\"   Fallback teks dari kolom klasifikasi: {empty_mask.sum()} baris\")\n",
    "    else:\n",
    "        print(\"   Tidak ada baris dengan teks kosong setelah cleaning.\")\n",
    "\n",
    "if col_nama:\n",
    "    print(\"   Mendeteksi kebocoran nama pribadi...\")\n",
    "    name_leak_mask = df.apply(lambda row: is_likely_name(row[\"job_text_raw\"], row[col_nama]), axis=1)\n",
    "    leaked_count = name_leak_mask.sum()\n",
    "    print(f\"   Terdeteksi {leaked_count} baris dengan indikasi nama pribadi -> dihapus.\")\n",
    "    df = df[~name_leak_mask].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f565470",
   "metadata": {},
   "source": [
    "## 7. Pelabelan Kelas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8b6bbdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3/4] Pelabelan kelas...\n",
      "   350 baris dilabeli dengan rule-based fallback.\n",
      "\n",
      "Total data valid: 358 baris\n",
      "\n",
      "Distribusi label keseluruhan:\n",
      "label\n",
      "Wirausaha Informatika    128\n",
      "Non-IT                   124\n",
      "Programmer                98\n",
      "Data Analyst               8\n"
     ]
    }
   ],
   "source": [
    "print(\"[3/4] Pelabelan kelas...\")\n",
    "\n",
    "if col_klasifikasi:\n",
    "    df[\"label\"] = df[col_klasifikasi].str.strip().map(KLASIFIKASI_MAP)\n",
    "    missing = df[\"label\"].isna()\n",
    "    if missing.any():\n",
    "        df.loc[missing, \"label\"] = df.loc[missing, \"job_text_raw\"].apply(classify_rule_based)\n",
    "        print(f\"   {missing.sum()} baris dilabeli dengan rule-based fallback.\")\n",
    "else:\n",
    "    df[\"label\"] = df[\"job_text_raw\"].apply(classify_rule_based)\n",
    "    print(\"   Semua baris dilabeli dengan rule-based (tidak ada kolom klasifikasi).\")\n",
    "\n",
    "df = df[df[\"job_text_raw\"].str.len() >= 3].copy()\n",
    "result = (\n",
    "    df[[col_nim, col_jabatan, \"job_text_raw\", \"label\"]]\n",
    "    .rename(columns={col_nim: \"nim\"})\n",
    "    .dropna(subset=[\"nim\", \"label\"])\n",
    "    .drop_duplicates(subset=[\"nim\"], keep=\"first\")\n",
    ")\n",
    "\n",
    "print(f\"\\nTotal data valid: {len(result)} baris\")\n",
    "print(\"\\nDistribusi label keseluruhan:\")\n",
    "print(result[\"label\"].value_counts().to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c803ba9",
   "metadata": {},
   "source": [
    "## 8. Train/Test Split & Simpan Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5849e195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4/4] Train/Test Split (70:30, stratified)...\n",
      "   Training set : 250 baris -> d:\\Project\\sista_mif_ta\\data\\processed\\training_corpus.csv\n",
      "   Test set     : 108 baris -> d:\\Project\\sista_mif_ta\\data\\processed\\test_set.csv\n",
      "\n",
      "DATA PENGUJIAN (TEST SET):\n",
      "  Programmer                : 30\n",
      "  Data Analyst              : 2 < 5\n",
      "  Wirausaha Informatika     : 39\n",
      "  Non-IT                    : 37\n"
     ]
    }
   ],
   "source": [
    "print(\"[4/4] Train/Test Split (70:30, stratified)...\")\n",
    "\n",
    "min_count = result[\"label\"].value_counts().min()\n",
    "if min_count < 2:\n",
    "    train_df, test_df = train_test_split(result, test_size=0.30, random_state=42)\n",
    "else:\n",
    "    train_df, test_df = train_test_split(result, test_size=0.30, stratify=result[\"label\"], random_state=42)\n",
    "\n",
    "cols_out = [\"nim\", \"job_text_raw\", \"label\"]\n",
    "train_df[cols_out].to_csv(OUTPUT_DIR / \"training_corpus.csv\", index=False, sep=\";\", encoding=ENCODING)\n",
    "test_df[cols_out].to_csv(OUTPUT_DIR / \"test_set.csv\", index=False, sep=\";\", encoding=ENCODING)\n",
    "\n",
    "print(f\"   Training set : {len(train_df)} baris -> {OUTPUT_DIR / 'training_corpus.csv'}\")\n",
    "print(f\"   Test set     : {len(test_df)} baris -> {OUTPUT_DIR / 'test_set.csv'}\")\n",
    "\n",
    "print(\"\\nDATA PENGUJIAN (TEST SET):\")\n",
    "for cls in TARGET_CLASSES:\n",
    "    c = test_df[\"label\"].value_counts().get(cls, 0)\n",
    "    flag = \" < 5\" if c < 5 else \"\"\n",
    "    print(f\"  {cls:25} : {c}{flag}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3f4559",
   "metadata": {},
   "source": [
    "## 9. Visualisasi Distribusi Kelas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17be8acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "COLORS = [\"#4C72B0\", \"#DD8452\", \"#55A868\", \"#C44E52\"]\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
    "fig.suptitle(\"Distribusi Kelas Profil Karir Alumni MIF\", fontsize=14, fontweight=\"bold\", y=1.02)\n",
    "\n",
    "for ax, (df_plot, title) in zip(axes, [\n",
    "    (result, \"Keseluruhan\"),\n",
    "    (train_df, \"Training Set (70%)\"),\n",
    "    (test_df, \"Test Set (30%)\"),\n",
    "]):\n",
    "    counts = df_plot[\"label\"].value_counts().reindex(TARGET_CLASSES, fill_value=0)\n",
    "    bars = ax.bar(counts.index, counts.values, color=COLORS, edgecolor=\"white\", linewidth=0.8)\n",
    "    ax.set_title(title, fontsize=12, fontweight=\"bold\")\n",
    "    ax.set_ylabel(\"Jumlah Data\")\n",
    "    ax.set_ylim(0, counts.max() * 1.2)\n",
    "    ax.tick_params(axis=\"x\", rotation=15)\n",
    "    ax.yaxis.set_major_locator(mticker.MaxNLocator(integer=True))\n",
    "    for bar, val in zip(bars, counts.values):\n",
    "        ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.5,\n",
    "                str(val), ha=\"center\", va=\"bottom\", fontsize=10, fontweight=\"bold\")\n",
    "    ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cb8fd260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Corpus berhasil disiapkan dan disimpan.\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "fig.suptitle(\"Proporsi Kelas (Pie Chart)\", fontsize=13, fontweight=\"bold\")\n",
    "\n",
    "for i, (df_plot, title) in enumerate([(train_df, \"Training Set\"), (test_df, \"Test Set\")]):\n",
    "    counts = df_plot[\"label\"].value_counts().reindex(TARGET_CLASSES, fill_value=0)\n",
    "    ax[i].pie(\n",
    "        counts.values,\n",
    "        labels=counts.index,\n",
    "        autopct=\"%1.1f%%\",\n",
    "        colors=COLORS,\n",
    "        startangle=140,\n",
    "        wedgeprops={\"edgecolor\": \"white\", \"linewidth\": 1.5}\n",
    "    )\n",
    "    ax[i].set_title(title, fontsize=11, fontweight=\"bold\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nCorpus berhasil disiapkan dan disimpan.\")"
   ]
  }
 ],
 "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}