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All Journal BIOTROPIA - The Southeast Asian Journal of Tropical Biology Jurnal Tekno Kompak Journal of Informatics, Information System, Software Engineering and Applications (INISTA) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Premium: Insurance Business Journal Journal of Computer Science and Informatics Engineering Jurnal Pengabdian Masyarakat Ilmu Komputer
Istiqomah, Nalar
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Veterinary

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Journal : Journal of Computer Science and Informatics Engineering

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Comparative Performance of IndoBERT and IndoLEM Baseline Models for Post-Disaster Health Information Extraction from Indonesian Online News Istiqomah, Nalar; Novika, Fanny
Journal of Computer Science and Informatics Engineering Vol 4 No 3 (2025): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i3.1174

Abstract

Natural disasters often have significant impacts on public health, yet systematic monitoring of post-disaster diseases in Indonesia remains limited. This study compares the performance of two Named Entity Recognition (NER) models in extracting health impacts, affected locations, and disaster types from Indonesian-language online news articles. The first model is IndoBERT, fine-tuned using 1,137 manually validated disaster-related news articles. The second comprises baseline models from the IndoLEM benchmark, namely mBERT and XLM-RoBERTa, without domain-specific training. Evaluation results show that IndoBERT outperforms the baseline models, achieving 90.00% accuracy and an F1-score of 88.26%, compared to mBERT (72.93%) and XLM-R (76.44%). Further analysis of the extracted entities reveals spatial and temporal disease trends: floods in Java are consistently associated with diarrhea and skin diseases, while volcanic eruptions in eastern Indonesia are linked to respiratory infections and hypertension. These findings highlight the importance of selecting appropriate models to support data-driven public health monitoring systems in disaster-prone regions
Co-Authors Fanny Novika Imas Sukaesih Sitanggang Lailan Syaufina Novika, Fanny