381 lines
13 KiB
Plaintext
381 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "JVPdWpz3hhbj"
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},
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"source": [
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "4Mvva3v65h1v"
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},
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"source": [
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"# **UNIGRAM**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "1cub_VJnUJMl",
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"outputId": "1889eb61-4f3f-4780-f42e-02368076cce3"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Corpus: saya suka makan nasi\n",
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"Tokens (4): ['saya', 'suka', 'makan', 'nasi']\n",
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"\n",
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"Frekuensi Unigram dalam kalimat\n",
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" ('saya'): 1\n",
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" ('suka'): 1\n",
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" ('makan'): 1\n",
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" ('nasi'): 1\n",
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"\n",
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"Total unigram dalam 1 kalimat: 4\n",
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"\n",
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"Probabilitas masing-masing unigram:\n",
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" P(saya) = 0.25 (25.00%)\n",
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" P(suka) = 0.25 (25.00%)\n",
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" P(makan) = 0.25 (25.00%)\n",
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" P(nasi) = 0.25 (25.00%)\n",
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"\n",
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"Probabilitas Keseluruhan Kalimat (Model Unigram):\n",
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" P(saya suka makan nasi) = P(saya)=0.25 x P(suka)=0.25 x P(makan)=0.25 x P(nasi)=0.25 = 0.0039 (0.39%)\n"
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]
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}
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],
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"source": [
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"from collections import Counter\n",
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"from IPython.display import clear_output\n",
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"import math\n",
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"\n",
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"# 1. Input Kalimat dan Tokenisasi\n",
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"kalimat = input(\"Masukkan kalimat: \").strip()\n",
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"\n",
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"# Bersihkan output (khusus lingkungan notebook)\n",
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"try:\n",
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" clear_output()\n",
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"except:\n",
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" pass\n",
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"\n",
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"print(f\"Corpus: {kalimat}\")\n",
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"\n",
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"# Tokenize\n",
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"tokens = kalimat.lower().split()\n",
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"print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
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"\n",
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"# 2. Hitung Frekuensi Unigram\n",
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"unigram_counts = Counter(tokens)\n",
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"total_tokens = sum(unigram_counts.values())\n",
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"\n",
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"print(\"\\nFrekuensi Unigram dalam kalimat\")\n",
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"for pair, count in unigram_counts.items():\n",
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" print(f\" ('{pair}'): {count}\")\n",
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"print(f\"\\nTotal unigram dalam 1 kalimat: {total_tokens}\")\n",
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"\n",
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"# 3. Hitung Probabilitas Unigram: P(wi) = Count(wi) / Total Kata\n",
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"unigram_probabilities = {}\n",
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"for word, count in unigram_counts.items():\n",
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" prob = count / total_tokens\n",
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" unigram_probabilities[word] = prob\n",
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"\n",
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"print(\"\\nProbabilitas masing-masing unigram:\")\n",
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"for word, prob in unigram_probabilities.items():\n",
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" print(f\" P({word}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
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"\n",
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"# 4. Hitung Probabilitas Kalimat Keseluruhan (P(kalimat) = P(w1) * P(w2) * ...)\n",
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"p_kalimat = 1\n",
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"prob_parts = []\n",
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"\n",
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"# Loop untuk menghitung probabilitas total dan membangun string rumus detail\n",
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"for word in tokens:\n",
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" prob_value = unigram_probabilities[word]\n",
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" p_kalimat *= prob_value\n",
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" # Format: P(word)=prob_value\n",
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" prob_parts.append(f\"P({word})={prob_value:.2f}\")\n",
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"\n",
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"# Gabungkan bagian-bagian rumus untuk mendapatkan prob_str detail\n",
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"prob_str = \" x \".join(prob_parts)\n",
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"\n",
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"print(\"\\nProbabilitas Keseluruhan Kalimat (Model Unigram):\")\n",
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"print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.4f} ({p_kalimat*100:.2f}%)\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Vstwt996-FrS"
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},
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"source": [
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"# **BIGRAM**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "XRIY4qgTVbjl",
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"outputId": "ea6e62ce-45a0-40c9-ca98-1fcc30558479"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Corpus: saya makan nasi dan saya makan roti\n",
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"Tokens (7): ['saya', 'makan', 'nasi', 'dan', 'saya', 'makan', 'roti']\n",
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"\n",
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"Frekuensi Bigram dalam kalimat:\n",
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" ('saya', 'makan'): 2\n",
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" ('makan', 'nasi'): 1\n",
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" ('nasi', 'dan'): 1\n",
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" ('dan', 'saya'): 1\n",
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" ('makan', 'roti'): 1\n",
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"\n",
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"Total bigram dalam 1 kalimat: 6\n",
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"\n",
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"Probabilitas masing-masing bigram:\n",
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" P(makan|saya) = 1.00 (100.00%)\n",
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" P(nasi|makan) = 0.50 (50.00%)\n",
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" P(dan|nasi) = 1.00 (100.00%)\n",
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" P(saya|dan) = 1.00 (100.00%)\n",
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" P(roti|makan) = 0.50 (50.00%)\n",
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"\n",
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"Probabilitas Keseluruhan Kalimat (Model Bigram):\n",
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" P(saya makan nasi dan saya makan roti) = P(saya)=0.29 x P(makan|saya)=1.00 x P(nasi|makan)=0.50 x P(dan|nasi)=1.00 x P(saya|dan)=1.00 x P(makan|saya)=1.00 x P(roti|makan)=0.50 = 0.071429 (7.14%)\n"
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]
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}
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],
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"source": [
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"from collections import Counter\n",
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"from IPython.display import clear_output\n",
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"import math\n",
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"\n",
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"# 1. Input Kalimat dan Tokenisasi\n",
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"kalimat = input(\"Masukkan kalimat: \").strip()\n",
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"\n",
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"# Bersihkan output (khusus lingkungan notebook)\n",
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"try:\n",
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" clear_output()\n",
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"except:\n",
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" pass\n",
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"\n",
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"print(f\"Corpus: {kalimat}\")\n",
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"\n",
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"# Tokenisasi\n",
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"tokens = kalimat.lower().split()\n",
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"print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
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"\n",
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"# 2. Hitung Frekuensi Unigram dan Bigram\n",
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"unigram_counts = Counter(tokens)\n",
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"bigrams = [(tokens[i], tokens[i+1]) for i in range(len(tokens) - 1)]\n",
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"bigram_counts = Counter(bigrams)\n",
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"\n",
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"print(\"\\nFrekuensi Bigram dalam kalimat:\")\n",
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"for pair, count in bigram_counts.items():\n",
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" print(f\" {pair}: {count}\")\n",
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"print(f\"\\nTotal bigram dalam 1 kalimat: {sum(bigram_counts.values())}\")\n",
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"\n",
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"# 3. Hitung Probabilitas Bigram: P(w2 | w1) = Count(w1,w2) / Count(w1)\n",
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"bigram_probabilities = {}\n",
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"for (w1, w2), count in bigram_counts.items():\n",
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" prob = count / unigram_counts[w1]\n",
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" bigram_probabilities[(w1, w2)] = prob\n",
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"\n",
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"print(\"\\nProbabilitas masing-masing bigram:\")\n",
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"for (w1, w2), prob in bigram_probabilities.items():\n",
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" print(f\" P({w2}|{w1}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
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"\n",
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"# 4. Hitung Probabilitas Kalimat Keseluruhan (Model Bigram)\n",
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"# P(kalimat) = P(w1) * P(w2|w1) * P(w3|w2) * ...\n",
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"total_tokens = sum(unigram_counts.values())\n",
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"p_w1 = unigram_counts.get(tokens[0], 0) / total_tokens # P(w1)\n",
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"p_kalimat = p_w1 # Inisialisasi dengan P(w1)\n",
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"\n",
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"prob_str_parts = [f\"P({tokens[0]})={p_w1:.2f}\"] # Tambahkan P(w1) ke rumus\n",
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"\n",
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"for i in range(1, len(tokens)):\n",
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" pair = (tokens[i-1], tokens[i])\n",
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" p = bigram_probabilities.get(pair, 0)\n",
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" p_kalimat *= p\n",
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" prob_str_parts.append(f\"P({pair[1]}|{pair[0]})={p:.2f}\")\n",
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"\n",
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"# Gabungkan rumus perkalian untuk ditampilkan\n",
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"prob_str = \" x \".join(prob_str_parts)\n",
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"\n",
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"print(\"\\nProbabilitas Keseluruhan Kalimat (Model Bigram):\")\n",
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"print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.6f} ({p_kalimat*100:.2f}%)\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "E6n1IU8X-G9S"
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},
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"source": [
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"# **TRIGRAM**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "BIRARsj2FHJg",
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"outputId": "968d420e-9370-40e5-e7e1-148e1d351d62"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Corpus: mahasiswa mengerjakan tugas kemudian mahasiswa upload e-learning\n",
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"Tokens (7): ['mahasiswa', 'mengerjakan', 'tugas', 'kemudian', 'mahasiswa', 'upload', 'e-learning']\n",
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"\n",
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"Frekuensi Trigram dalam kalimat:\n",
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" ('mahasiswa', 'mengerjakan', 'tugas'): 1\n",
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" ('mengerjakan', 'tugas', 'kemudian'): 1\n",
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" ('tugas', 'kemudian', 'mahasiswa'): 1\n",
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" ('kemudian', 'mahasiswa', 'upload'): 1\n",
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" ('mahasiswa', 'upload', 'e-learning'): 1\n",
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"\n",
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"Total trigram dalam 1 kalimat: 5\n",
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"\n",
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"Probabilitas masing-masing trigram:\n",
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" P(tugas|mahasiswa,mengerjakan) = 1.00 (100.00%)\n",
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" P(kemudian|mengerjakan,tugas) = 1.00 (100.00%)\n",
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" P(mahasiswa|tugas,kemudian) = 1.00 (100.00%)\n",
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" P(upload|kemudian,mahasiswa) = 1.00 (100.00%)\n",
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" P(e-learning|mahasiswa,upload) = 1.00 (100.00%)\n",
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"\n",
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"Probabilitas Keseluruhan Kalimat (Model Trigram):\n",
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" P(mahasiswa mengerjakan tugas kemudian mahasiswa upload e-learning) = P(mahasiswa)=0.29 x P(mengerjakan|mahasiswa)=0.50 x P(tugas|mahasiswa,mengerjakan)=1.00 x P(kemudian|mengerjakan,tugas)=1.00 x P(mahasiswa|tugas,kemudian)=1.00 x P(upload|kemudian,mahasiswa)=1.00 x P(e-learning|mahasiswa,upload)=1.00 = 0.142857 (14.29%)\n"
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]
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}
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],
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"source": [
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"from collections import Counter\n",
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"from IPython.display import clear_output\n",
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"import math\n",
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"\n",
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"# 1. Input Kalimat dan Tokenisasi\n",
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"kalimat = input(\"Masukkan kalimat: \").strip()\n",
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"\n",
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"# Bersihkan output (khusus lingkungan notebook)\n",
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"try:\n",
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" clear_output()\n",
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"except:\n",
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" pass\n",
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"\n",
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"print(f\"Corpus: {kalimat}\")\n",
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"\n",
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"# Tokenisasi\n",
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"tokens = kalimat.lower().split()\n",
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"print(f\"Tokens ({len(tokens)}): {tokens}\")\n",
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"\n",
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"# 2. Hitung Frekuensi Bigram dan Trigram\n",
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"bigrams = [(tokens[i], tokens[i+1]) for i in range(len(tokens) - 1)]\n",
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"trigrams = [(tokens[i], tokens[i+1], tokens[i+2]) for i in range(len(tokens) - 2)]\n",
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"\n",
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"bigram_counts = Counter(bigrams)\n",
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"trigram_counts = Counter(trigrams)\n",
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"\n",
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"print(\"\\nFrekuensi Trigram dalam kalimat:\")\n",
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"for tg, count in trigram_counts.items():\n",
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" print(f\" {tg}: {count}\")\n",
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"print(f\"\\nTotal trigram dalam 1 kalimat: {sum(trigram_counts.values())}\")\n",
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"\n",
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"# 3. Hitung Probabilitas Trigram: P(w3 | w1, w2) = Count(w1,w2,w3) / Count(w1,w2)\n",
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"trigram_probabilities = {}\n",
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"for (w1, w2, w3), count in trigram_counts.items():\n",
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" # Hindari pembagian dengan nol (jika ada bigram yang tidak muncul)\n",
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" if bigram_counts[(w1, w2)] > 0:\n",
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" prob = count / bigram_counts[(w1, w2)]\n",
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" else:\n",
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" prob = 0\n",
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" trigram_probabilities[(w1, w2, w3)] = prob\n",
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"\n",
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"print(\"\\nProbabilitas masing-masing trigram:\")\n",
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"for (w1, w2, w3), prob in trigram_probabilities.items():\n",
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" print(f\" P({w3}|{w1},{w2}) = {prob:.2f} ({prob*100:.2f}%)\")\n",
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"\n",
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"# Tambahkan perhitungan Unigram Count (dibutuhkan untuk P(w1) dan P(w2|w1))\n",
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"unigram_counts = Counter(tokens)\n",
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"total_tokens = sum(unigram_counts.values())\n",
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"\n",
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"# 4. Hitung Probabilitas Kalimat Keseluruhan (Model Trigram)\n",
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"# P(kalimat) = P(w1) * P(w2|w1) * P(w3|w1,w2) * ...\n",
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"\n",
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"# a. P(w1)\n",
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"p_w1 = unigram_counts.get(tokens[0], 0) / total_tokens if total_tokens > 0 else 0\n",
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"\n",
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"# b. P(w2|w1) (Menggunakan Bigram tanpa smoothing)\n",
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"if len(tokens) > 1:\n",
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" count_w1 = unigram_counts.get(tokens[0], 1) # Hindari pembagian dengan 0\n",
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" p_w2_w1 = bigram_counts.get((tokens[0], tokens[1]), 0) / count_w1\n",
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"else:\n",
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" p_w2_w1 = 1.0 # Jika hanya 1 kata\n",
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"\n",
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"p_kalimat = p_w1 * p_w2_w1 # Inisialisasi dengan P(w1) * P(w2|w1)\n",
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"\n",
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"# Daftar bagian rumus untuk ditampilkan\n",
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"prob_str_parts = [f\"P({tokens[0]})={p_w1:.2f}\"]\n",
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"if len(tokens) > 1:\n",
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" prob_str_parts.append(f\"P({tokens[1]}|{tokens[0]})={p_w2_w1:.2f}\")\n",
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"\n",
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"# c. Perkalian Trigram P(wi | wi-2, wi-1) untuk i >= 3\n",
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"for i in range(len(tokens) - 2):\n",
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" triplet = (tokens[i], tokens[i+1], tokens[i+2])\n",
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" p = trigram_probabilities.get(triplet, 0)\n",
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" p_kalimat *= p\n",
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" prob_str_parts.append(f\"P({triplet[2]}|{triplet[0]},{triplet[1]})={p:.2f}\")\n",
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"\n",
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"prob_str = \" x \".join(prob_str_parts)\n",
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"\n",
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"print(\"\\nProbabilitas Keseluruhan Kalimat (Model Trigram):\")\n",
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"print(f\" P({' '.join(tokens)}) = {prob_str} = {p_kalimat:.6f} ({p_kalimat*100:.2f}%)\")\n"
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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