From 0d282059304ea2a61cdc0c1cea7dc493c85b089e Mon Sep 17 00:00:00 2001
From: "Muhammad.haekal Rahmatullah" <haekalrahmatullah36@gmail.com>
Date: Mon, 2 Nov 2026 01:41:24 -0500
Subject: [PATCH] Upload files to "/"

---
 Indeks_Gapeka.py         | 128 ++++++++++++++++++++++++
 LAPORAN GAPEKA 2026.docx | Bin 0 -> 23636 bytes
 cari_gapeka.py           | 146 +++++++++++++++++++++++++++
 evaluasi_gapeka.py       | 206 +++++++++++++++++++++++++++++++++++++++
 4 files changed, 480 insertions(+)
 create mode 100644 Indeks_Gapeka.py
 create mode 100644 LAPORAN GAPEKA 2026.docx
 create mode 100644 cari_gapeka.py
 create mode 100644 evaluasi_gapeka.py

diff --git a/Indeks_Gapeka.py b/Indeks_Gapeka.py
new file mode 100644
index 0000000..b93dad8
--- /dev/null
+++ b/Indeks_Gapeka.py
@@ -0,0 +1,128 @@
+import random
+from pathlib import Path
+
+# FUNGSI MEMBACA PDF
+def baca_pdf(path):
+    reader = PdfReader(path)
+    return "\n".join(
+        (p.extract_text() or "")
+        for p in reader.pages
+    )
+
+# FUNGSI MEMOTONG TEKS (CHUNKING)
+def potong(teks, ukuran=800, tumpang=100):
+    hasil = []
+    mulai = 0
+    while mulai < len(teks):
+        hasil.append(teks[mulai:mulai + ukuran])
+        mulai += ukuran - tumpang
+    return [h for h in hasil if h.strip()]
+
+# MENCARI PDF DI FOLDER DOKUMEN
+daftar_pdf = list(Path("dokumen").glob("*.pdf"))
+
+if not daftar_pdf:
+    print("❌ Tidak ada file PDF di folder 'dokumen'! Pastikan Anda membuat folder bernama 'dokumen' dan memasukkan file PDF terkait KAI di dalamnya.")
+else:
+    # Mengambil PDF pertama
+    pdf = daftar_pdf[0]
+    
+    # Membaca isi PDF
+    teks = baca_pdf(pdf)
+    
+    print(f"\n📄 Nama PDF: {pdf.name}")
+    print(f"📊 Total karakter: {len(teks)}")
+    
+    # Menampilkan 500 karakter pertama
+    print("\n===== 500 KARAKTER PERTAMA =====")
+    print(teks[:500])
+    
+    # Eksperimen tiga ukuran chunk
+    for ukuran in (500, 1000, 1500):
+        overlap = int(ukuran * 0.1)
+        chunks = potong(
+            teks,
+            ukuran=ukuran,
+            tumpang=overlap
+        )
+        
+        print("\n" + "=" * 50)
+        print(f"UKURAN CHUNK: {ukuran}")
+        print(f"OVERLAP: {overlap}")
+        print(f"JUMLAH CHUNK: {len(chunks)}")
+        
+        # Mengambil maksimal 3 chunk secara acak
+        sampel = random.sample(
+            range(len(chunks)),
+            min(3, len(chunks))
+        )
+        
+        for nomor in sampel:
+            print(f"\n--- CHUNK {nomor} ---")
+            print(chunks[nomor])
+
+
+# LAB 2: EMBEDDING DAN CHROMADB
+if daftar_pdf:
+    # Model embedding multilingual
+    emb = embedding_functions.SentenceTransformerEmbeddingFunction(
+        model_name="paraphrase-multilingual-MiniLM-L12-v2"
+    )
+    
+    # Membuat database lokal
+    client = chromadb.PersistentClient(path="./chroma_db")
+    
+    def bangun_indeks(nama_koleksi, ukuran):
+        # Menghapus koleksi lama agar tidak tercampur
+        try:
+            client.delete_collection(name=nama_koleksi)
+        except Exception:
+            pass
+        
+        # Membuat koleksi baru
+        koleksi = client.create_collection(
+            name=nama_koleksi,
+            embedding_function=emb,
+            metadata={"hnsw:space": "cosine"}
+        )
+        
+        for pdf in daftar_pdf:
+            teks = baca_pdf(pdf)
+            overlap = int(ukuran * 0.1)
+            chunks = potong(
+                teks,
+                ukuran=ukuran,
+                tumpang=overlap
+            )
+            
+            if not chunks:
+                print(pdf.name, "tidak memiliki teks.")
+                continue
+            
+            koleksi.upsert(
+                ids=[
+                    f"{pdf.stem}-{ukuran}-{i}"
+                    for i in range(len(chunks))
+                ],
+                documents=chunks,
+                metadatas=[
+                    {
+                        "sumber": pdf.name,
+                        "chunk": i,
+                        "ukuran": ukuran
+                    }
+                    for i in range(len(chunks))
+                ]
+            )
+            
+            print(pdf.name, "->", len(chunks), "chunk")
+            
+        print(f"\n✅ Koleksi '{nama_koleksi}' berhasil dibuat.")
+        print("Total data dalam koleksi:", koleksi.count())
+    
+    # Membuat dua koleksi untuk eksperimen
+    bangun_indeks("pedoman_500", 500)
+    bangun_indeks("pedoman_1000", 1000)
+
+else:
+    print("⚠️ Indeks gagal dibuat karena tidak ada file PDF.")
\ No newline at end of file
diff --git a/LAPORAN GAPEKA 2026.docx b/LAPORAN GAPEKA 2026.docx
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diff --git a/cari_gapeka.py b/cari_gapeka.py
new file mode 100644
index 0000000..1d9e791
--- /dev/null
+++ b/cari_gapeka.py
@@ -0,0 +1,146 @@
+import math
+import re
+from collections import Counter
+
+
+class KAIGapekaRAG:
+    """Sistem RAG Sederhana berbasis Pure Python untuk topik GAPEKA 2026 PT KAI."""
+
+    def __init__(self, knowledge_base):
+        print("[INFO] Menginisialisasi Basis Pengetahuan KAI Gapeka 2026...")
+        self.documents = knowledge_base
+        self.vocab = self._build_vocab(self.documents)
+        # Pra-penghitungan vektor TF-IDF untuk dokumen lokal
+        self.doc_vectors = [
+            self._compute_tfidf(doc, self.vocab, self.documents)
+            for doc in self.documents
+        ]
+
+    def _tokenize(self, text):
+        """Membersihkan teks dan memecahnya menjadi kata-kata (token)."""
+        return re.findall(r"\b\w+\b", text.lower())
+
+    def _build_vocab(self, docs):
+        """Membangun kamus kata unik dari seluruh dokumen."""
+        vocab = set()
+        for doc in docs:
+            vocab.update(self._tokenize(doc))
+        return sorted(list(vocab))
+
+    def _compute_tfidf(self, text, vocab, all_docs):
+        """Menghitung bobot TF-IDF secara manual tanpa library eksternal."""
+        tokens = self._tokenize(text)
+        tf = Counter(tokens)
+        total_tokens = len(tokens) if tokens else 1
+
+        vector = {}
+        for word in vocab:
+            # Term Frequency (TF)
+            term_freq = tf[word] / total_tokens
+
+            # Inverse Document Frequency (IDF)
+            doc_count = sum(1 for d in all_docs if word in self._tokenize(d))
+            idf = (
+                math.log(len(all_docs) / (doc_count + 1)) + 1 if doc_count > 0 else 0
+            )
+
+            vector[word] = term_freq * idf
+        return vector
+
+    def _cosine_similarity(self, vec1, vec2):
+        """Menghitung kemiripan kosinus antara dua vektor TF-IDF."""
+        intersection = set(vec1.keys()).intersection(set(vec2.keys()))
+        numerator = sum(vec1[x] * vec2[x] for x in intersection)
+
+        sum1 = sum(val ** 2 for val in vec1.values())
+        sum2 = sum(val ** 2 for val in vec2.values())
+        denominator = math.sqrt(sum1) * math.sqrt(sum2)
+
+        if denominator == 0:
+            return 0.0
+        return numerator / denominator
+
+    def retrieve(self, query, top_k=1):
+        """Mencari dokumen paling relevan menggunakan Cosine Similarity."""
+        query_vector = self._compute_tfidf(query, self.vocab, self.documents)
+        scores = []
+
+        for i, doc_vec in enumerate(self.doc_vectors):
+            score = self._cosine_similarity(query_vector, doc_vec)
+            scores.append((score, self.documents[i]))
+
+        # Urutkan berdasarkan skor tertinggi
+        scores.sort(key=lambda x: x[0], reverse=True)
+        return [doc for score, doc in scores[:top_k]]
+
+    def generate_answer(self, query):
+        """Tahap Generation: Menggabungkan hasil retrieval dengan format jawaban RAG."""
+        retrieved_docs = self.retrieve(query, top_k=1)
+        context = (
+            retrieved_docs[0]
+            if retrieved_docs
+            else "Maaf, informasi tidak ditemukan."
+        )
+
+        # Template Generator Extractive RAG
+        answer = (
+            f"==================================================\n"
+            f"HASIL GENERASI RAG - PT KERETA API INDONESIA (GAPEKA 2026)\n"
+            f"==================================================\n"
+            f"Pertanyaan : {query}\n\n"
+            f"Konteks Relevan (Retrieved):\n-> {context}\n\n"
+            f"Jawaban Sistem:\n"
+            f"Berdasarkan informasi resmi kebijakan GAPEKA 2026 PT KAI, "
+            f"berikut adalah detail informasinya:\n"
+            f'"{context}"\n'
+            f"=================================================="
+        )
+        return answer, retrieved_docs
+
+
+# ==========================================
+# EKSEKUSI PROGRAM UTAMA
+# ==========================================
+if __name__ == "__main__":
+    # 1. Dataset / Basis Pengetahuan Lokal KAI GAPEKA 2026
+    kai_kb = [
+        (
+            "PT Kereta Api Indonesia (KAI) resmi memberlakukan Grafik Perjalanan "
+            "Kereta Api (GAPEKA) 2026 mulai tanggal 1 Desember 2026."
+        ),
+        (
+            "Tiket perjalanan kereta api untuk jadwal GAPEKA 2026 sudah dapat "
+            "dipesan secara online melalui aplikasi Access by KAI."
+        ),
+        (
+            "Terdapat penyesuaian serta penyederhanaan nama pada beberapa "
+            "perjalanan kereta api agar lebih simpel dan mudah diingat oleh "
+            "penumpang."
+        ),
+        (
+            "KAI menambah titik pemberhentian di sejumlah stasiun untuk "
+            "mendukung mobilitas antarkota dan kawasan aglomerasi."
+        ),
+        (
+            "KAI menghadirkan layanan rute baru seperti KA Borobudur "
+            "(Lempuyangan–Gambir PP, fakultatif) dan KA Pandawa "
+            "(Yogyakarta–Semarang Tawang PP) yang mulai beroperasi pada 1 "
+            "Desember 2026."
+        ),
+    ]
+
+    # 2. Inisialisasi Sistem RAG
+    rag = KAIGapekaRAG(kai_kb)
+
+    # 3. Contoh Daftar Pengujian Pertanyaan Tugas RAG
+    daftar_pertanyaan = [
+        "Kapan GAPEKA 2026 mulai diberlakukan dan di mana bisa pesan tiketnya?",
+        "Apa saja rute kereta api baru yang dihadirkan KAI dalam GAPEKA 2026?",
+        "Bagaimana kebijakan terkait nama kereta api dalam GAPEKA 2026?"
+    ]
+
+    # 4. Menjalankan Pipeline Pengujian
+    for idx, sample_query in enumerate(daftar_pertanyaan, 1):
+        print(f"\n[PENGUJIAN {idx}]")
+        final_answer, sources = rag.generate_answer(sample_query)
+        print(final_answer)
\ No newline at end of file
diff --git a/evaluasi_gapeka.py b/evaluasi_gapeka.py
new file mode 100644
index 0000000..354dfc1
--- /dev/null
+++ b/evaluasi_gapeka.py
@@ -0,0 +1,206 @@
+import math
+import re
+from collections import Counter
+
+
+class KAIGapekaRAG:
+    """Sistem RAG Sederhana berbasis Pure Python untuk topik GAPEKA 2026 PT KAI."""
+
+    def __init__(self, knowledge_base):
+        print("[INFO] Menginisialisasi Basis Pengetahuan KAI Gapeka 2026...")
+        self.documents = knowledge_base
+        self.vocab = self._build_vocab(self.documents)
+        # Pra-penghitungan vektor TF-IDF untuk dokumen lokal
+        self.doc_vectors = [
+            self._compute_tfidf(doc, self.vocab, self.documents)
+            for doc in self.documents
+        ]
+
+    def _tokenize(self, text):
+        """Membersihkan teks dan memecahnya menjadi kata-kata (token)."""
+        return re.findall(r"\b\w+\b", text.lower())
+
+    def _build_vocab(self, docs):
+        """Membangun kamus kata unik dari seluruh dokumen."""
+        vocab = set()
+        for doc in docs:
+            vocab.update(self._tokenize(doc))
+        return sorted(list(vocab))
+
+    def _compute_tfidf(self, text, vocab, all_docs):
+        """Menghitung bobot TF-IDF secara manual tanpa library eksternal."""
+        tokens = self._tokenize(text)
+        tf = Counter(tokens)
+        total_tokens = len(tokens) if tokens else 1
+
+        vector = {}
+        for word in vocab:
+            # Term Frequency (TF)
+            term_freq = tf[word] / total_tokens
+
+            # Inverse Document Frequency (IDF)
+            doc_count = sum(1 for d in all_docs if word in self._tokenize(d))
+            idf = (
+                math.log(len(all_docs) / (doc_count + 1)) + 1 if doc_count > 0 else 0
+            )
+
+            vector[word] = term_freq * idf
+        return vector
+
+    def _cosine_similarity(self, vec1, vec2):
+        """Menghitung kemiripan kosinus antara dua vektor TF-IDF."""
+        intersection = set(vec1.keys()).intersection(set(vec2.keys()))
+        numerator = sum(vec1[x] * vec2[x] for x in intersection)
+
+        sum1 = sum(val ** 2 for val in vec1.values())
+        sum2 = sum(val ** 2 for val in vec2.values())
+        denominator = math.sqrt(sum1) * math.sqrt(sum2)
+
+        if denominator == 0:
+            return 0.0
+        return numerator / denominator
+
+    def retrieve_with_scores(self, query, top_k=5):
+        """Mengembalikan daftar dokumen dan skornya berdasarkan Cosine Similarity."""
+        query_vector = self._compute_tfidf(query, self.vocab, self.documents)
+        scores = []
+
+        for i, doc_vec in enumerate(self.doc_vectors):
+            score = self._cosine_similarity(query_vector, doc_vec)
+            scores.append((score, self.documents[i]))
+
+        # Urutkan berdasarkan skor tertinggi
+        scores.sort(key=lambda x: x[0], reverse=True)
+        return scores[:top_k]
+
+    def generate_answer(self, query):
+        """Tahap Generation: Menggabungkan hasil retrieval dengan format jawaban RAG."""
+        retrieved_scores = self.retrieve_with_scores(query, top_k=1)
+        context = (
+            retrieved_scores[0][1]
+            if retrieved_scores and retrieved_scores[0][0] > 0
+            else "Maaf, informasi tidak ditemukan."
+        )
+
+        answer = (
+            f"==================================================\n"
+            f"HASIL GENERASI RAG - PT KERETA API INDONESIA (GAPEKA 2026)\n"
+            f"==================================================\n"
+            f"Pertanyaan : {query}\n\n"
+            f"Konteks Relevan (Retrieved):\n-> {context}\n\n"
+            f"Jawaban Sistem:\n"
+            f"Berdasarkan informasi resmi kebijakan GAPEKA 2026 PT KAI, "
+            f"berikut adalah detail informasinya:\n"
+            f'"{context}"\n'
+            f"=================================================="
+        )
+        return answer, context
+
+
+# ==========================================
+# MODUL EVALUASI RETRIEVAL (Hit@K & MRR)
+# ==========================================
+
+# Daftar Uji (Pertanyaan dan kata kunci yang harus ada di dokumen yang diretrubusi)
+UJI = [
+    ("Kapan GAPEKA 2026 mulai diberlakukan?", "1 desember 2026"),
+    ("Di mana bisa pesan tiket kereta GAPEKA 2026?", "access by kai"),
+    ("Apa ada perubahan nama kereta api?", "penyederhanaan nama"),
+    ("Apakah ada penambahan titik pemberhentian stasiun?", "titik pemberhentian"),
+    ("Sebutkan rute kereta api baru yang beroperasi!", "ka borobudur"),
+]
+
+
+def cari_peringkat(rag_system, pertanyaan, kunci, k=5):
+    """Mencari peringkat dokumen pertama yang mengandung kata kunci."""
+    hasil = rag_system.retrieve_with_scores(pertanyaan, top_k=k)
+    kunci_bersih = " ".join(kunci.lower().split())
+    
+    for peringkat, (score, doc) in enumerate(hasil, start=1):
+        doc_bersih = " ".join(doc.lower().split())
+        if kunci_bersih in doc_bersih and score > 0:
+            return peringkat
+    return None
+
+
+def hit_at_k(rag_system, uji_data, k=3):
+    """Menghitung metrik Hit@K."""
+    benar = 0
+    for pertanyaan, kunci in uji_data:
+        peringkat = cari_peringkat(rag_system, pertanyaan, kunci, k)
+        if peringkat is not None:
+            benar += 1
+    return benar / len(uji_data)
+
+
+def hitung_mrr(rag_system, uji_data):
+    """Menghitung metrik Mean Reciprocal Rank (MRR@5)."""
+    total = 0
+    for pertanyaan, kunci in uji_data:
+        peringkat = cari_peringkat(rag_system, pertanyaan, kunci, 5)
+        if peringkat is not None:
+            total += 1 / peringkat
+    return total / len(uji_data)
+
+
+# ==========================================
+# EKSEKUSI PROGRAM UTAMA
+# ==========================================
+if __name__ == "__main__":
+    # 1. Dataset / Basis Pengetahuan Lokal KAI GAPEKA 2026
+    kai_kb = [
+        (
+            "PT Kereta Api Indonesia (KAI) resmi memberlakukan Grafik Perjalanan "
+            "Kereta Api (GAPEKA) 2026 mulai tanggal 1 Desember 2026."
+        ),
+        (
+            "Tiket perjalanan kereta api untuk jadwal GAPEKA 2026 sudah dapat "
+            "dipesan secara online melalui aplikasi Access by KAI."
+        ),
+        (
+            "Terdapat penyesuaian serta penyederhanaan nama pada beberapa "
+            "perjalanan kereta api agar lebih simpel dan mudah diingat oleh penumpang."
+        ),
+        (
+            "KAI menambah titik pemberhentian di sejumlah stasiun untuk "
+            "mendukung mobilitas antarkota dan kawasan aglomerasi."
+        ),
+        (
+            "KAI menghadirkan layanan rute baru seperti KA Borobudur "
+            "(Lempuyangan–Gambir PP, fakultatif) dan KA Pandawa "
+            "(Yogyakarta–Semarang Tawang PP) yang mulai beroperasi pada 1 Desember 2026."
+        ),
+    ]
+
+    # 2. Inisialisasi Sistem RAG
+    rag = KAIGapekaRAG(kai_kb)
+
+    # 3. Contoh Uji Jalankan Pipeline RAG Tunggal
+    sample_query = "Kapan GAPEKA 2026 mulai diberlakukan dan di mana bisa pesan tiketnya?"
+    final_answer, sources = rag.generate_answer(sample_query)
+    print("\n" + final_answer)
+
+    # 4. Evaluasi Performa Retrieval (Hit@K & MRR)
+    print("\n" + "=" * 50)
+    print("EVALUASI PERFORMA RETRIEVAL KAI GAPEKA 2026")
+    print("=" * 50)
+    print(f"Jumlah Pertanyaan Uji : {len(UJI)}")
+
+    for k in (1, 3, 5):
+        nilai_hit = hit_at_k(rag, UJI, k)
+        print(f"Hit@{k} = {nilai_hit:.2f}")
+
+    mrr_score = hitung_mrr(rag, UJI)
+    print(f"MRR@5 = {mrr_score:.2f}")
+
+    print("\nPERTANYAAN YANG GAGAL DITEMUKAN (Jika ada):")
+    gagal = False
+    for pertanyaan, kunci in UJI:
+        peringkat = cari_peringkat(rag, pertanyaan, kunci, 5)
+        if peringkat is None:
+            gagal = True
+            print(f"- {pertanyaan}")
+    
+    if not gagal:
+        print("Semua pertanyaan berhasil ditemukan oleh sistem retrieval!")
+    print("=" * 50)
\ No newline at end of file