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All Journal JOIN (Jurnal Online Informatika) ABDIMAS Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika
Edwin Lesmana Tjiong
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Computer Science & IT Economics, Econometrics & Finance Education Public Health Social Sciences

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Pelatihan Pengenalan Konsep Pembangunan Game Bagi Murid SMK Sint Joseph Harya Bima Dirgantara; Muhammad Adrinta Abdurrazzaq; Tedi Lesmana Marselino; Yulia Ery Kurniawati; Edwin Lesmana Tjiong
ABDIMAS Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2023): Pengabdian Kepada Masyarakat
Publisher : Research and Community Service INSTITUT TEKNOLOGI DAN BISNIS KALBIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53008/abdimas.v4i1.1800

Abstract

Pengabdian Kepada Masyarakat (PKM) merupakan salah satu kegiatan wajib bagi dosen berdasarkan Tri Dharma Perguruan Tinggi. Artikel ini membahas mengenai perlunya mengadakan pelatihan berkaitan dengan pengenalan konsep pembangunan video game. Hal ini dianggap perlu karena melihat banyaknya peluang yang terbuka di industri video game. Ditambah lagi dengan fakta bahwa Indonesia merupakan pangsa pasar video game terbesar di Asia Tenggara. Pengembang video game lokal bahkan hanya menguasai 0.4% pangsa pasar di Indonesia. Disisi lain, Indonesia memiliki kurikulum pendidikan yang bertujuan untuk menyiapkan sumberdaya manusianya untuk menguasai kemampuan industri dengan pendidikan setingkat Sekolah Menengag Atas (SMA) yang disebut Sekolah Menengah Kejuruan (SMK). Motivasi yang kuat untuk menguasai kemampuan industri pada pelajar SMK menjadikan SMK menjadi target yang cocok untuk diberikan pembekalan mengenai pembangunan video game. Maka dari itu, Institut Teknolgi dan Bisnis Kalbis serta SMK Sint Joseph bermitra untuk mengadakan pelatihan ini. Dari kegiatan PKM yang telah dilakukan, kegiatan ini memiliki hasil evaluasi yang sangat baik berdasarkan kuisioner yang diisi peserta. Berdasarkan antusiasme dan saran dari peserta membuat pelatihan ini juga layak untuk diadakan kembali dengan materi pembelajaran yang baru dan lebih mendalam.
LLM-Based Information Retrieval for Disease Detection Using Semantic Similarity Muhammad Adrinta Abdurrazzaq; Edwin Lesmana Tjiong; Kent Algren Wanady
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1486

Abstract

Information retrieval systems are vital for disease prediction, but traditional methods like TF-IDF struggle with word meanings and produce long, complex vectors. This research uses Large Language Models (LLMs) and follows the CRISP-DM methodology to improve accuracy. Using health forum discussions labeled with specific diseases, we split the data into queries and a corpus. Semantic similarity is used to retrieve the most relevant text from the corpus. After preprocessing, we compare LLMs and TF-IDF, with LLMs achieving an accuracy of 0.911 (Top-K=30), outperforming TF-IDF. LLMs excel by creating shorter, meaningful vectors that preserve context, enabling precise semantic matching. These results demonstrate LLMs' potential to enhance healthcare information retrieval, offering more accurate and context-aware solutions. This research highlights how advanced AI can overcome traditional methods' limitations, opening new possibilities for medical informatics.
An Indonesian Chatbot for Disease Diagnosis Using Retrieval-Augmented Generation Muhammad Adrinta Abdurrazzaq; Edwin Lesmana Tjiong; Aulia Fasya; Michelle Hiu; Joses Tanuwidjaya
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9nnkn955

Abstract

The rapid advancement of Large Language Models (LLMs) has enabled their use in medical information systems, although challenges such as hallucinations, domain mismatches, and the lack of a verified knowledge base remain significant, particularly in low-source languages ​​like Indonesian. This study introduces an Indonesian-language medical chatbot based on the open-source GPT-OSS-20B model enhanced through a Retrieval-Augmented Generation (RAG) pipeline. The system combines semantic retrieval using jina-embeddings-v3, lexical re-ranking with the BM25 algorithm, and a lightweight Logistic Regression-based domain filter as an initial filter to prevent out-of-domain LLM usage. Evaluation using Indonesian medical articles and annotated patient-doctor conversations shows that the domain filter works well on synthetic data but results in misclassification of natural queries. A hybrid weighted reranker (FAISS L2 + BM25) performed the best with a Top-30 accuracy of 0.699. Black-box testing indicates that the system flow functions as designed, although the response quality has not been validated by clinical experts. These findings suggest that RAG-based open-source LLMs can improve access to Indonesian-language medical information, but still have important limitations such as the lack of clinical validation, potential errors in scraped data, and suboptimal robustness of domain filters.
Co-Authors Aulia Fasya Harya Bima Dirgantara Joses Tanuwidjaya Kent Algren Wanady Michelle Hiu Muhammad Adrinta Abdurrazzaq Muhammad Adrinta Abdurrazzaq Tedi Lesmana Marselino Yulia Ery Kurniawati