diff --git a/N_Gram f.ipynb b/N_Gram f.ipynb new file mode 100644 index 0000000..802ef9b --- /dev/null +++ b/N_Gram f.ipynb @@ -0,0 +1,394 @@ +{ + "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 +} \ No newline at end of file