336 lines
9.1 KiB
Plaintext
336 lines
9.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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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": "qBYcPYAb059g",
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"outputId": "9f57b704-da1b-4495-d366-24c30586dc76",
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Masukkan jumlah dokumen yang ingin dimasukkan: 4\n"
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]
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}
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],
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"source": [
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"# Input jumlah dokumen\n",
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"import pandas as pd\n",
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"n = int(input(\"Masukkan jumlah dokumen yang ingin dimasukkan: \"))"
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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": 4,
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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": "mo-yt5Ob1N8j",
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"outputId": "362ac3e0-d84b-4014-db96-cc3b10ecdb32"
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},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Masukkan teks untuk dokumen ke-1: saya adalah seorang pria\n",
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"Masukkan teks untuk dokumen ke-2: saya adalah pria yang memiliki hati\n",
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"Masukkan teks untuk dokumen ke-3: hati saya telah terisi satu nama\n",
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"Masukkan teks untuk dokumen ke-4: di dalam hati saya terukir nama pasangan saya\n"
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]
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},
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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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"\n",
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"=== Dokumen yang Dimasukkan ===\n",
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"Doc 1: saya adalah seorang pria\n",
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"Doc 2: saya adalah pria yang memiliki hati\n",
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"Doc 3: hati saya telah terisi satu nama\n",
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"Doc 4: di dalam hati saya terukir nama pasangan saya\n"
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]
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}
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],
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"source": [
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"# Input teks dokumen satu per satu\n",
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"documents = []\n",
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"for i in range(n):\n",
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" teks = input(f\"Masukkan teks untuk dokumen ke-{i+1}: \")\n",
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" documents.append(teks)\n",
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"\n",
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"print(\"\\n=== Dokumen yang Dimasukkan ===\")\n",
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"for i, doc in enumerate(documents):\n",
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" print(f\"Doc {i+1}: {doc}\")"
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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": 5,
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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": "FkmxRAFq1oDK",
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"outputId": "62c4508e-1725-4f30-fbdb-4de8072498b2"
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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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"\n",
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"=== Hasil Tokenisasi ===\n",
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"Doc 1: ['saya', 'adalah', 'seorang', 'pria']\n",
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"Doc 2: ['saya', 'adalah', 'pria', 'yang', 'memiliki', 'hati']\n",
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"Doc 3: ['hati', 'saya', 'telah', 'terisi', 'satu', 'nama']\n",
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"Doc 4: ['di', 'dalam', 'hati', 'saya', 'terukir', 'nama', 'pasangan', 'saya']\n"
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]
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}
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],
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"source": [
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"# Tahap Tokenisasi\n",
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"tokenized_docs = []\n",
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"for doc in documents:\n",
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" tokens = doc.lower().split()\n",
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" tokenized_docs.append(tokens)\n",
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"\n",
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"print(\"\\n=== Hasil Tokenisasi ===\")\n",
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"for i, tokens in enumerate(tokenized_docs):\n",
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" print(f\"Doc {i+1}: {tokens}\")"
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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": 6,
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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": "ybC1Vo2C_c3q",
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"outputId": "fa31c57e-5364-4ded-fcd0-54d0db46c34b"
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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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"\n",
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"=== Corpus Keseluruhan (Semua Kata dari Semua Dokumen) ===\n",
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"['saya', 'adalah', 'seorang', 'pria', 'saya', 'adalah', 'pria', 'yang', 'memiliki', 'hati', 'hati', 'saya', 'telah', 'terisi', 'satu', 'nama', 'di', 'dalam', 'hati', 'saya', 'terukir', 'nama', 'pasangan', 'saya']\n",
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"Jumlah total kata dalam seluruh dokumen: 24\n"
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]
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}
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],
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"source": [
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"# Pembuatan Corpus\n",
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"corpus_all = [word for doc in tokenized_docs for word in doc]\n",
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"\n",
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"print(\"\\n=== Corpus Keseluruhan (Semua Kata dari Semua Dokumen) ===\")\n",
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"print(corpus_all)\n",
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"print(f\"Jumlah total kata dalam seluruh dokumen: {len(corpus_all)}\")"
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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": 7,
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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": "s6S-Ma4R1xuq",
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"outputId": "98c3685b-1798-4038-d17e-6e45ca419b51"
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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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"\n",
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"=== Vocabulary (Kata Unik) ===\n",
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"['adalah', 'dalam', 'di', 'hati', 'memiliki', 'nama', 'pasangan', 'pria', 'satu', 'saya', 'seorang', 'telah', 'terisi', 'terukir', 'yang']\n",
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"Jumlah kata unik (vocabulary size): 15\n",
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"\n",
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"=== Vocabulary (Kata Unik) ===\n",
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" 1. adalah\n",
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" 2. dalam\n",
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" 3. di\n",
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" 4. hati\n",
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" 5. memiliki\n",
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" 6. nama\n",
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" 7. pasangan\n",
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" 8. pria\n",
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" 9. satu\n",
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"10. saya\n",
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"11. seorang\n",
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"12. telah\n",
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"13. terisi\n",
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"14. terukir\n",
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"15. yang\n",
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"\n",
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"Jumlah kata unik (vocabulary size): 15\n"
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]
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}
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],
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"source": [
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"# Pembuatan Vocabulary\n",
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"vocabulary = sorted(set(corpus_all))\n",
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"\n",
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"print(\"\\n=== Vocabulary (Kata Unik) ===\")\n",
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"print(vocabulary)\n",
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"print(f\"Jumlah kata unik (vocabulary size): {len(vocabulary)}\")\n",
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"\n",
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"\n",
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"vocabulary = sorted(set(corpus_all))\n",
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"\n",
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"print(\"\\n=== Vocabulary (Kata Unik) ===\")\n",
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"for idx, word in enumerate(vocabulary, start=1):\n",
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" print(f\"{idx:>2}. {word}\")\n",
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"print(f\"\\nJumlah kata unik (vocabulary size): {len(vocabulary)}\")"
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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": 10,
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"metadata": {
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"id": "ShevCTva2Fg9"
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},
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"outputs": [],
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"source": [
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"# Representasi Numerik (Matriks BoW)\n",
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"bow_matrix = []\n",
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"for doc in tokenized_docs:\n",
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" vector = [doc.count(word) for word in vocabulary]\n",
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" bow_matrix.append(vector)"
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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": 11,
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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": "-yB6D2pY2M0E",
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"outputId": "b6b2f4d3-da8b-4aee-e9ce-034def4d5cf7"
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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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"\n",
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"=== Matriks Bag of Words ===\n",
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" adalah dalam di hati memiliki nama pasangan pria satu saya \\\n",
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"D1 1 0 0 0 0 0 0 1 0 1 \n",
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"D2 1 0 0 1 1 0 0 1 0 1 \n",
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"D3 0 0 0 1 0 1 0 0 1 1 \n",
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"D4 0 1 1 1 0 1 1 0 0 2 \n",
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"\n",
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" seorang telah terisi terukir yang \n",
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"D1 1 0 0 0 0 \n",
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"D2 0 0 0 0 1 \n",
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"D3 0 1 1 0 0 \n",
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"D4 0 0 0 1 0 \n"
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]
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}
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],
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"source": [
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"df_bow = pd.DataFrame(bow_matrix, columns=vocabulary)\n",
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"df_bow.index = [f\"D{i}\" for i in range(1, len(documents)+1)] # ubah label indeks jadi D1, D2, D3\n",
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"\n",
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"print(\"\\n=== Matriks Bag of Words ===\")\n",
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"print(df_bow)"
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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": 12,
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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": "8ruf5vKL2rGD",
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"outputId": "65a4674e-1c01-4833-ec55-f66f77b8b6c2"
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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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"\n",
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"=== Tabel Frekuensi Kata (Keseluruhan Dokumen) ===\n",
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" Kata Frekuensi\n",
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"0 saya 5\n",
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"1 hati 3\n",
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"2 nama 2\n",
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"3 pria 2\n",
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"4 adalah 2\n",
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"5 di 1\n",
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"6 dalam 1\n",
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"7 pasangan 1\n",
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"8 memiliki 1\n",
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"9 satu 1\n",
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"10 seorang 1\n",
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"11 telah 1\n",
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"12 terisi 1\n",
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"13 terukir 1\n",
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"14 yang 1\n",
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"Frekuensi kata: 15\n"
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]
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}
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],
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"source": [
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"# Membuat Tabel Frekuensi Kata (Total dari seluruh dokumen)\n",
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"word_frequencies = df_bow.sum().sort_values(ascending=False).reset_index()\n",
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"word_frequencies.columns = [\"Kata\", \"Frekuensi\"]\n",
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"\n",
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"print(\"\\n=== Tabel Frekuensi Kata (Keseluruhan Dokumen) ===\")\n",
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"print(word_frequencies)\n",
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"print(f\"Frekuensi kata: {len(word_frequencies)}\")"
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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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"id": "NQjExannHuj0"
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},
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"outputs": [],
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"source": []
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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": "3.12.12"
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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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