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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Pendidikan Matematika dan IPA INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Teknik Informatika (JUTIF) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Masyarakat Sehat Indonesia Proceeding Seminar Nasional IPA
Dyah Susilowati
Faculty of Pharmacy, Universitas Setia Budi, Jl. Letjen Sutoyo, Surakarta 57127
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Health Professions Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

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A Locally Grounded Retrieval-Augmented LLM-Based Chatbot for Bilingual Stunting Prevention Consultation among Health Cadres in Indonesia Tanwir, Tanwir; Hidjah, Khasnur; Susilowati, Dyah; Anggrawan, Anthony; Sulistianingsih, Neny
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5459

Abstract

Stunting remains a major public health challenge in Indonesia, affecting 21.6% of children under five nationally and 18.34% in Nusa Tenggara Barat (NTB), which strains the capacity of health cadres to deliver timely and accurate nutrition education. This study aims to develop a consultation chatbot by integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to provide context-aware stunting prevention guidance. A total of 45 journal articles and 7 books were curated to construct 7,642 question–answer pairs using a RAG-based pipeline. Text preprocessing involved segmentation, embedding, and Byte Pair Encoding tokenization, followed by fine-tuning a LLaMA 3 model on an NVIDIA L4 GPU. Model performance was evaluated using ROUGE and BERTScore metrics, complemented by a small pilot usability assessment. The RAG-integrated model achieved a ROUGE-1 score of 81.03% and a BERTScore F1 of 93.48%, consistently outperforming baseline models. These findings demonstrate the potential of RAG-enhanced LLMs to support scalable and accessible health informatics solutions for empowering health cadres in resource-limited and rural settings.
Co-Authors Abdul Karim Abdul Rahim Aguswandi, Lalu Heri Anthony Anggrawan Bambang Suryadi, Bambang Chani Saputri, Dian Syafitri Helna Wardhana Husain Husain Irhas Irhas, Irhas Khasnur Hidjah Marlina Marlina Muhamad Wisnu Alfiansyah Muhammad Maulana MUHAMMAD TAUFIK Muti'ah, Muti'ah Neny Sulistianingsih Samsul Hadi Susi Endrawati Tanwir, Tanwir Winarsih Winarsih