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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer)
Pasaribu, Yuni Rumyanti
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Computer Science & IT

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PENERAPAN K-MEANS CLUSTERING UNTUK SEGMENTASI WILAYAH BERDASARKAN JUMLAH KEJADIAN BENCANA ALAM DI SUMATERA UTARA Manalu, Christ Natamaro Anastasius; Pasaribu, Yuni Rumyanti; Malika, Salsa; Tamba, Saut Parsaoran
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1977

Abstract

North Sumatra is a disaster-prone province in Indonesia, frequently affected by floods, landslides, and extreme weather events. This study employs the K-Means Clustering algorithm to classify 33 districts/cities in North Sumatra based on disaster frequency. The results reveal three distinct clusters: Cluster 0 (low risk), Cluster 1 (high flood and extreme disaster risk), and Cluster 2 (high landslide risk). Model validation using a silhouette coefficient of 0.63 and the elbow method confirms the reliability of the clustering. These findings provide a scientific basis for region-specific disaster mitigation strategies, emphasizing infrastructure development for flood-prone areas and slope stabilization for landslide-prone zones. Future research should incorporate socioeconomic factors and temporal analysis to enhance disaster risk assessment.
Co-Authors Malika, Salsa Manalu, Christ Natamaro Anastasius Tamba, Saut Parsaoran