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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal on Software Engineering (IJSE) Jurnal Komputer Terapan Jurnal Riset Informatika JSAI (Journal Scientific and Applied Informatics) Juriti Prima (Jurnal Ilmiah Teknik Industri Prima) bit-Tech JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of International Conference Proceedings Jurnal Indonesia Sosial Teknologi Jurnal Manajemen Sistem Informasi Mutiara: Jurnal Penelitian dan Karya Ilmiah
Joko Sutopo
Universitas Teknologi Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Development of Hidden Markov Model Based Hydrometeorological Disaster Prediction Model for More Effective Preparedness Joko Sutopo; Burhanuddin Mohd Aboobaider; Mustaqim Pabbajah; Juhansar Juhansar; Aprijanto Aprijanto; RB Hendri Kuswantoro
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.6359

Abstract

Hydrometeorological disasters, such as floods, are significant threats that require an accurate prediction system to improve preparedness and risk mitigation. This research aims to develop a Hidden Markov Model (HMM)-based hydrometeorological disaster prediction model by utilizing hydrological data from Kelambu Dam in Demak Regency. The data used includes water levels upstream (Level Up), downstream (Level Down), and water discharge (Q Serang), as well as information on flood events in the period 2022-2024. The methods applied include data collection and preprocessing, model training using the Baum-Welch algorithm, and performance evaluation with accuracy, precision, recall, and F1-score metrics. The results showed that the HMM model was able to identify hydrological change patterns and predict flood events with a high level of accuracy, reaching 94.29% at the best iteration. The performance evaluation also indicated that the model has a good balance between precision and recall, making it a potential tool in early warning systems. Thus, the implementation of this prediction model can improve community preparedness and support decision-making in hydrometeorological disaster management.
Co-Authors Agra Laksmi Maitri Agung Yuliyanto Nugroho Ahmad Taufiq Rohman Alvin Andrian Rivaldi Anindya Nurindah Hidayati Annisa Rachman Aprijanto Aprijanto Aulia Nur Septiani Bambang Moertono Setiawan Burhanuddin Mohd Aboobaider Daffa Sholah Islamey Delfi Arviana Wulan Prastika Desi Ayu Rahmadani Fitria Shinta Devi Fransiscus Harri Dwi Kurniawan Man Hermanus Erwan Bali Loko Juhansar Andi Latief M. A. Burhanuddin Mohd Khanapi Abd Ghani Muhammad Faishal Tsaqief Muhammad Habsyi Mubarak Mustaqim Pabbajah Naufal Syafiq Fadillah Noviana Salsabila Nur Rohman RB Hendri Kuswantoro Rebecca Angelina Silaen Ritzqy karina Sanusi Mulyo Widodo Silvan Abimanyu Sri Uszdevita Syardillah Pohan Tundo, Tundo Zulhawati