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 (Informatika, Universitas Jenderal Soedirman, 2026)
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.