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All Journal ILKOM Jurnal Ilmiah Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jambura Journal of Electrical and Electronics Engineering Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Minfo Polgan (JMP) Jurnal Nasional Cosphi Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Journal Of Informatics And Busisnes Empiris Jurnal Pengabdian Pada Masyarakat (EJPPM) Jurnal Pengabdian Pada Masyarakat Eldimas
Maryam Hasan
Universitas Ichsan Gorontalo
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Grouping of Areas Based on Flood Disaster Level Using K-Means Clustering Algorithm Hasan, Maryam; Panna, Sudirman S.; Haba, Abd. Rahmat Karim; Alhamad, Apriyanto
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 Almer Hassan Ali Amiruddin Bengnga Apriyanto Alhamad Bahrin Bahrin Betrisandi, Betrisandi Gaffar, Gaffar Haba, Abd. Rahmat Karim Husdi Ismail S Manto Juriko Abdussamad Kartika Chandra Pelangi Mahmud, Mohamad An Nafli Muh Faisal Muhammad Erdiansyah Panna, sudirman S. Ramadhani, Syarifah Fitrah Rifdah Rofifah Faruk Abdullah Salihi, Irvan Siska Udilawati Sudirman S Panna Swastiani Dunggio Syahrir Abdussamad Warid Yunus Yulianti Lasena Zulfrianto Yusrin Lamasigi