{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVPdWpz3hhbj"
      },
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Mvva3v65h1v"
      },
      "source": [
        "# **UNIGRAM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "1cub_VJnUJMl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a712acbd-01e2-4c9e-f2c0-d7d33f3bc9fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corpus: Jangan pernah berhenti belajar, karena hidup tak pernah berhenti mengajarkan\n",
            "Tokens (10): ['jangan', 'pernah', 'berhenti', 'belajar,', 'karena', 'hidup', 'tak', 'pernah', 'berhenti', 'mengajarkan']\n",
            "\n",
            "Frekuensi Unigram dalam kalimat\n",
            " ('jangan'): 1\n",
            " ('pernah'): 2\n",
            " ('berhenti'): 2\n",
            " ('belajar,'): 1\n",
            " ('karena'): 1\n",
            " ('hidup'): 1\n",
            " ('tak'): 1\n",
            " ('mengajarkan'): 1\n",
            "\n",
            "Total unigram dalam 1 kalimat: 10\n",
            "\n",
            "Probabilitas masing-masing unigram:\n",
            " P(jangan) = 0.10 (10.00%)\n",
            " P(pernah) = 0.20 (20.00%)\n",
            " P(berhenti) = 0.20 (20.00%)\n",
            " P(belajar,) = 0.10 (10.00%)\n",
            " P(karena) = 0.10 (10.00%)\n",
            " P(hidup) = 0.10 (10.00%)\n",
            " P(tak) = 0.10 (10.00%)\n",
            " P(mengajarkan) = 0.10 (10.00%)\n",
            "\n",
            "Probabilitas Keseluruhan Kalimat (Model Unigram):\n",
            " P(jangan pernah berhenti belajar, karena hidup tak pernah berhenti mengajarkan) = P(jangan)=0.10 x P(pernah)=0.20 x P(berhenti)=0.20 x P(belajar,)=0.10 x P(karena)=0.10 x P(hidup)=0.10 x P(tak)=0.10 x P(pernah)=0.20 x P(berhenti)=0.20 x P(mengajarkan)=0.10 = 0.0000 (0.00%)\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "from IPython.display import clear_output\n",
        "import math\n",
        "\n",
        "# 1. Input Kalimat dan Tokenisasi\n",
        "kalimat = input(\"Masukkan kalimat: Jangan pernah berhenti belajar, karena hidup tak pernah berhenti mengajarkan \").strip()\n",
        "\n",
        "# Bersihkan output (khusus lingkungan notebook)\n",
        "try:\n",
        "    clear_output()\n",
        "except:\n",
        "    pass\n",
        "\n",
        "print(f\"Corpus: {kalimat}\")\n",
        "\n",
        "# Tokenize\n",
        "tokens = kalimat.lower().split()\n",
        "print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
        "\n",
        "# 2. Hitung Frekuensi Unigram\n",
        "unigram_counts = Counter(tokens)\n",
        "total_tokens = sum(unigram_counts.values())\n",
        "\n",
        "print(\"\\nFrekuensi Unigram dalam kalimat\")\n",
        "for pair, count in unigram_counts.items():\n",
        "    print(f\" ('{pair}'): {count}\")\n",
        "print(f\"\\nTotal unigram dalam 1 kalimat: {total_tokens}\")\n",
        "\n",
        "# 3. Hitung Probabilitas Unigram: P(wi) = Count(wi) / Total Kata\n",
        "unigram_probabilities = {}\n",
        "for word, count in unigram_counts.items():\n",
        "    prob = count / total_tokens\n",
        "    unigram_probabilities[word] = prob\n",
        "\n",
        "print(\"\\nProbabilitas masing-masing unigram:\")\n",
        "for word, prob in unigram_probabilities.items():\n",
        "    print(f\" P({word}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
        "\n",
        "# 4. Hitung Probabilitas Kalimat Keseluruhan (P(kalimat) = P(w1) * P(w2) * ...)\n",
        "p_kalimat = 1\n",
        "prob_parts = []\n",
        "\n",
        "# Loop untuk menghitung probabilitas total dan membangun string rumus detail\n",
        "for word in tokens:\n",
        "    prob_value = unigram_probabilities[word]\n",
        "    p_kalimat *= prob_value\n",
        "    # Format: P(word)=prob_value\n",
        "    prob_parts.append(f\"P({word})={prob_value:.2f}\")\n",
        "\n",
        "# Gabungkan bagian-bagian rumus untuk mendapatkan prob_str detail\n",
        "prob_str = \" x \".join(prob_parts)\n",
        "\n",
        "print(\"\\nProbabilitas Keseluruhan Kalimat (Model Unigram):\")\n",
        "print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.4f} ({p_kalimat*100:.2f}%)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vstwt996-FrS"
      },
      "source": [
        "# **BIGRAM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRIY4qgTVbjl",
        "outputId": "4eff35ea-8a13-4b4a-fd8f-e0f3518c1add"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corpus: ilmu adalah cahaya, dan belajar adalah menyalakan lentera dalam kegelapan\n",
            "Tokens (10): ['ilmu', 'adalah', 'cahaya,', 'dan', 'belajar', 'adalah', 'menyalakan', 'lentera', 'dalam', 'kegelapan']\n",
            "\n",
            "Frekuensi Bigram dalam kalimat:\n",
            " ('ilmu', 'adalah'): 1\n",
            " ('adalah', 'cahaya,'): 1\n",
            " ('cahaya,', 'dan'): 1\n",
            " ('dan', 'belajar'): 1\n",
            " ('belajar', 'adalah'): 1\n",
            " ('adalah', 'menyalakan'): 1\n",
            " ('menyalakan', 'lentera'): 1\n",
            " ('lentera', 'dalam'): 1\n",
            " ('dalam', 'kegelapan'): 1\n",
            "\n",
            "Total bigram dalam 1 kalimat: 9\n",
            "\n",
            "Probabilitas masing-masing bigram:\n",
            " P(adalah|ilmu) = 1.00 (100.00%)\n",
            " P(cahaya,|adalah) = 0.50 (50.00%)\n",
            " P(dan|cahaya,) = 1.00 (100.00%)\n",
            " P(belajar|dan) = 1.00 (100.00%)\n",
            " P(adalah|belajar) = 1.00 (100.00%)\n",
            " P(menyalakan|adalah) = 0.50 (50.00%)\n",
            " P(lentera|menyalakan) = 1.00 (100.00%)\n",
            " P(dalam|lentera) = 1.00 (100.00%)\n",
            " P(kegelapan|dalam) = 1.00 (100.00%)\n",
            "\n",
            "Probabilitas Keseluruhan Kalimat (Model Bigram):\n",
            " P(ilmu adalah cahaya, dan belajar adalah menyalakan lentera dalam kegelapan) = P(ilmu)=0.10 x P(adalah|ilmu)=1.00 x P(cahaya,|adalah)=0.50 x P(dan|cahaya,)=1.00 x P(belajar|dan)=1.00 x P(adalah|belajar)=1.00 x P(menyalakan|adalah)=0.50 x P(lentera|menyalakan)=1.00 x P(dalam|lentera)=1.00 x P(kegelapan|dalam)=1.00 = 0.025000 (2.50%)\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "from IPython.display import clear_output\n",
        "import math\n",
        "\n",
        "# 1. Input Kalimat dan Tokenisasi\n",
        "kalimat = input(\"Masukkan kalimat: Ilmu adalah cahaya, dan belajar adalah menyalakan lentera dalam kegelapan \").strip()\n",
        "\n",
        "# Bersihkan output (khusus lingkungan notebook)\n",
        "try:\n",
        "    clear_output()\n",
        "except:\n",
        "    pass\n",
        "\n",
        "print(f\"Corpus: {kalimat}\")\n",
        "\n",
        "# Tokenisasi\n",
        "tokens = kalimat.lower().split()\n",
        "print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
        "\n",
        "# 2. Hitung Frekuensi Unigram dan Bigram\n",
        "unigram_counts = Counter(tokens)\n",
        "bigrams = [(tokens[i], tokens[i+1]) for i in range(len(tokens) - 1)]\n",
        "bigram_counts = Counter(bigrams)\n",
        "\n",
        "print(\"\\nFrekuensi Bigram dalam kalimat:\")\n",
        "for pair, count in bigram_counts.items():\n",
        "    print(f\" {pair}: {count}\")\n",
        "print(f\"\\nTotal bigram dalam 1 kalimat: {sum(bigram_counts.values())}\")\n",
        "\n",
        "# 3. Hitung Probabilitas Bigram: P(w2 | w1) = Count(w1,w2) / Count(w1)\n",
        "bigram_probabilities = {}\n",
        "for (w1, w2), count in bigram_counts.items():\n",
        "    prob = count / unigram_counts[w1]\n",
        "    bigram_probabilities[(w1, w2)] = prob\n",
        "\n",
        "print(\"\\nProbabilitas masing-masing bigram:\")\n",
        "for (w1, w2), prob in bigram_probabilities.items():\n",
        "    print(f\" P({w2}|{w1}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
        "\n",
        "# 4. Hitung Probabilitas Kalimat Keseluruhan (Model Bigram)\n",
        "#    P(kalimat) = P(w1) * P(w2|w1) * P(w3|w2) * ...\n",
        "total_tokens = sum(unigram_counts.values())\n",
        "p_w1 = unigram_counts.get(tokens[0], 0) / total_tokens # P(w1)\n",
        "p_kalimat = p_w1 # Inisialisasi dengan P(w1)\n",
        "\n",
        "prob_str_parts = [f\"P({tokens[0]})={p_w1:.2f}\"] # Tambahkan P(w1) ke rumus\n",
        "\n",
        "for i in range(1, len(tokens)):\n",
        "    pair = (tokens[i-1], tokens[i])\n",
        "    p = bigram_probabilities.get(pair, 0)\n",
        "    p_kalimat *= p\n",
        "    prob_str_parts.append(f\"P({pair[1]}|{pair[0]})={p:.2f}\")\n",
        "\n",
        "# Gabungkan rumus perkalian untuk ditampilkan\n",
        "prob_str = \" x \".join(prob_str_parts)\n",
        "\n",
        "print(\"\\nProbabilitas Keseluruhan Kalimat (Model Bigram):\")\n",
        "print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.6f} ({p_kalimat*100:.2f}%)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6n1IU8X-G9S"
      },
      "source": [
        "# **TRIGRAM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIRARsj2FHJg",
        "outputId": "6e09b998-b787-4c91-a710-57a809bf2223"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corpus: belajar adalah kunci membuka pintu kesuksesan\n",
            "Tokens (6): ['belajar', 'adalah', 'kunci', 'membuka', 'pintu', 'kesuksesan']\n",
            "\n",
            "Frekuensi Trigram dalam kalimat:\n",
            " ('belajar', 'adalah', 'kunci'): 1\n",
            " ('adalah', 'kunci', 'membuka'): 1\n",
            " ('kunci', 'membuka', 'pintu'): 1\n",
            " ('membuka', 'pintu', 'kesuksesan'): 1\n",
            "\n",
            "Total trigram dalam 1 kalimat: 4\n",
            "\n",
            "Probabilitas masing-masing trigram:\n",
            " P(kunci|belajar,adalah) = 1.00 (100.00%)\n",
            " P(membuka|adalah,kunci) = 1.00 (100.00%)\n",
            " P(pintu|kunci,membuka) = 1.00 (100.00%)\n",
            " P(kesuksesan|membuka,pintu) = 1.00 (100.00%)\n",
            "\n",
            "Probabilitas Keseluruhan Kalimat (Model Trigram):\n",
            " P(belajar adalah kunci membuka pintu kesuksesan) = P(belajar)=0.17 x P(adalah|belajar)=1.00 x P(kunci|belajar,adalah)=1.00 x P(membuka|adalah,kunci)=1.00 x P(pintu|kunci,membuka)=1.00 x P(kesuksesan|membuka,pintu)=1.00 = 0.166667 (16.67%)\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "from IPython.display import clear_output\n",
        "import math\n",
        "\n",
        "# 1. Input Kalimat dan Tokenisasi\n",
        "kalimat = input(\"Masukkan kalimat: Belajar adalah kunci membuka pintu kesuksesan\").strip()\n",
        "\n",
        "# Bersihkan output (khusus lingkungan notebook)\n",
        "try:\n",
        "    clear_output()\n",
        "except:\n",
        "    pass\n",
        "\n",
        "print(f\"Corpus: {kalimat}\")\n",
        "\n",
        "# Tokenisasi\n",
        "tokens = kalimat.lower().split()\n",
        "print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
        "\n",
        "# 2. Hitung Frekuensi Bigram dan Trigram\n",
        "bigrams = [(tokens[i], tokens[i+1]) for i in range(len(tokens) - 1)]\n",
        "trigrams = [(tokens[i], tokens[i+1], tokens[i+2]) for i in range(len(tokens) - 2)]\n",
        "\n",
        "bigram_counts = Counter(bigrams)\n",
        "trigram_counts = Counter(trigrams)\n",
        "\n",
        "print(\"\\nFrekuensi Trigram dalam kalimat:\")\n",
        "for tg, count in trigram_counts.items():\n",
        "    print(f\" {tg}: {count}\")\n",
        "print(f\"\\nTotal trigram dalam 1 kalimat: {sum(trigram_counts.values())}\")\n",
        "\n",
        "# 3. Hitung Probabilitas Trigram: P(w3 | w1, w2) = Count(w1,w2,w3) / Count(w1,w2)\n",
        "trigram_probabilities = {}\n",
        "for (w1, w2, w3), count in trigram_counts.items():\n",
        "    # Hindari pembagian dengan nol (jika ada bigram yang tidak muncul)\n",
        "    if bigram_counts[(w1, w2)] > 0:\n",
        "        prob = count / bigram_counts[(w1, w2)]\n",
        "    else:\n",
        "        prob = 0\n",
        "    trigram_probabilities[(w1, w2, w3)] = prob\n",
        "\n",
        "print(\"\\nProbabilitas masing-masing trigram:\")\n",
        "for (w1, w2, w3), prob in trigram_probabilities.items():\n",
        "    print(f\" P({w3}|{w1},{w2}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
        "\n",
        "# Tambahkan perhitungan Unigram Count (dibutuhkan untuk P(w1) dan P(w2|w1))\n",
        "unigram_counts = Counter(tokens)\n",
        "total_tokens = sum(unigram_counts.values())\n",
        "\n",
        "# 4. Hitung Probabilitas Kalimat Keseluruhan (Model Trigram)\n",
        "#    P(kalimat) = P(w1) * P(w2|w1) * P(w3|w1,w2) * ...\n",
        "\n",
        "# a. P(w1)\n",
        "p_w1 = unigram_counts.get(tokens[0], 0) / total_tokens if total_tokens > 0 else 0\n",
        "\n",
        "# b. P(w2|w1) (Menggunakan Bigram tanpa smoothing)\n",
        "if len(tokens) > 1:\n",
        "    count_w1 = unigram_counts.get(tokens[0], 1) # Hindari pembagian dengan 0\n",
        "    p_w2_w1 = bigram_counts.get((tokens[0], tokens[1]), 0) / count_w1\n",
        "else:\n",
        "    p_w2_w1 = 1.0 # Jika hanya 1 kata\n",
        "\n",
        "p_kalimat = p_w1 * p_w2_w1 # Inisialisasi dengan P(w1) * P(w2|w1)\n",
        "\n",
        "# Daftar bagian rumus untuk ditampilkan\n",
        "prob_str_parts = [f\"P({tokens[0]})={p_w1:.2f}\"]\n",
        "if len(tokens) > 1:\n",
        "    prob_str_parts.append(f\"P({tokens[1]}|{tokens[0]})={p_w2_w1:.2f}\")\n",
        "\n",
        "# c. Perkalian Trigram P(wi | wi-2, wi-1) untuk i >= 3\n",
        "for i in range(len(tokens) - 2):\n",
        "    triplet = (tokens[i], tokens[i+1], tokens[i+2])\n",
        "    p = trigram_probabilities.get(triplet, 0)\n",
        "    p_kalimat *= p\n",
        "    prob_str_parts.append(f\"P({triplet[2]}|{triplet[0]},{triplet[1]})={p:.2f}\")\n",
        "\n",
        "prob_str = \" x \".join(prob_str_parts)\n",
        "\n",
        "print(\"\\nProbabilitas Keseluruhan Kalimat (Model Trigram):\")\n",
        "print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.6f} ({p_kalimat*100:.2f}%)\")\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "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": 0
}