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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Repositor
Muhammad Daffa Nugraha
Universitas Muhammadiyah Malang
Computer Science & IT

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SC-Literature Intelligence: A Retrieval-Augmented Generation Framework for Multi-Category AI Literature Synthesis in Supply Chain Setio Basuki; Amelia Khoidir; Muhammad Ilham Perdana; Muhammad Daffa Nugraha; Masatoshi Tsuchiya
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16477

Abstract

This paper develops SC-Literature Intelligence, a retrieval-augmented generation (RAG) framework for research synthesis of scientific literature on artificial intelligence (AI) in the supply chain domain. The study addresses the fragmentation of scientific findings, which makes cross-document understanding difficult by supporting four categories of literature-analysis queries: trend analysis, gap detection, comparative synthesis, and evidence-based question answering (QA). The primary novelty lies in introducing a category-aware research synthesis framework capable of evaluating RAG performance across multiple literature-analysis tasks rather than conventional question answering. The framework is built from Scopus-indexed abstracts through pre-processing, chunk-based embedding using BGE-M3 and LaBSE, vector storage, semantic retrieval, and prompt-guided generation evaluated using the RAGAS framework across 640 experimental runs. The results show that BGE-M3 consistently outperforms LaBSE on all RAGAS indicators with the best configuration (chunk size 64, Top-K 5) achieving scores between 0.722 and 0.856 across faithfulness, answer relevancy, context precision, and context recall. Gap detection emerges as the best-supported query category, whereas comparative synthesis remains the most challenging. Failure analysis further reveals that retrieval-stage issues dominate over generation-stage issues, identifying embedding quality as the primary bottleneck. These findings demonstrate that category-aware RAG-based synthesis can support structured, evidence-grounded literature analysis in the supply chain AI domain.
Analisis Polarisasi Sentimen Kasus Bocornya Pusat Data Nasional Pada Platform Sosial Media Muhammad Daffa Nugraha; Setio Basuki; Mahar Faiqurahman
Jurnal Repositor Vol. 7 No. 4 (2025): November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v7i4.40851

Abstract

Kasus kebocoran data Pusat Data Nasional terjadi pada tahun 2024. Hal tersebut memicu berbagai reaksi masyarakat, terutama di media sosial YouTube. Analisis sentimen dilakukan untuk memahami berbagai opini tentang kebocoran data tersebut. Penelitian ini bertujuan menganalisis polarisasi sentimen masyarakat terkait kasus ini sebagai upaya memahami komentar terhadap isu keamanan data. Sebanyak 4.460 komentar dikumpulkan dari delapan video YouTube dengan latar tokoh berbeda, dan diklasifikasikan ke dalam empat kategori: positif, negatif, netral, dan others. Pengujian dilakukan dengan menggunakan IndoBERT dan IndoBERTweet dalam tiga skenario yang disesuaikan parameternya. Hasil terbaik diperoleh pada skenario pertama menggunakan IndoBERTweet dengan akurasi 0.87, sedangkan IndoBERT mendapatkan hasil 0.86. Analisis error menunjukkan bahwa kesalahan klasifikasi sering terjadi karena kata-kata yang bermakna umum dan ambigu, sehingga model kesulitan menentukan konteks sebenarnya dari komentar tersebut.
Co-Authors Amelia Khoidir Basuki, Setio Mahar Faiqurahman Masatoshi Tsuchiya Muhammad Ilham Perdana