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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JSAI (Journal Scientific and Applied Informatics) Jambura Journal of Electrical and Electronics Engineering Jurnal Nasional Cosphi Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Journal Of Informatics And Busisnes
Sudirman S. Panna
Universitas Ichsan Gorontalo
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering

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Grouping of Areas Based on Flood Disaster Level Using K-Means Clustering Algorithm Maryam Hasan; Sudirman S. Panna; Abd. Rahmat Karim Haba; Apriyanto Alhamad
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.33145

Abstract

The Province of Gorontalo is highly vulnerable to flood disasters due to its geographical conditions, high rainfall, and uncontrolled land-use changes. This study aims to apply the K-Means Clustering algorithm to classify regions based on flood impact levels to support disaster mitigation and decision-making processes by the National Search and Rescue Agency (BNPP) Gorontalo. The dataset comprises 405 disaster incident records obtained from related institutions, including the number of affected, injured, deceased, and missing individuals. The analysis process involves data collection, preprocessing, distance calculation using the Euclidean Distance method, and the formation of two clusters based on impact levels. The iteration process stopped at the second iteration, indicating that a stable (convergent) condition had been achieved. The results revealed that Cluster 1 (C1) includes areas significantly affected by floods such as Imana, Iloheluma, and Tudi villages, while Cluster 2 (C2) represents unaffected areas like Wapalo, Ilomata, Motihelumo, and others. The implementation of the K-Means algorithm proved effective in identifying disaster-prone regions objectively and data-driven, thus supporting more efficient disaster response planning.
Co-Authors Abd. Rahmat Karim Haba Almer Hassan Ali Apriyanto Alhamad Betrisandi, Betrisandi Haditsah Annur Hariati Husain Husdi Kamase, Hamka Witri Kartika Chandra Pelangi lahabu, sintiya Lepi, Andriyanto Maryam Hasan Mohamat Hamet Helingo Ningrum Muj Qurnia Shandy Rahmad Harun Rezqiwati Ishak Rofiq Harun salim, Muh Siska Udilawati Sudirman Melangi Yusrianto Malago