Implemntasi K-Means Clustering Untuk Menentukan Tingkat Bencana Rawan Banjir di Wilayah Sumatera Utara

Reimer Fernando Purba (Universitas Methodist Indonesia)
Astri Agnes Panjaitan (Universitas Methodist Indonesia)
Labora Triasi Butar-Butar (Universitas Methodist Indonesia)
John Franz E.D.L. Sitorus (Universitas Methodist Indonesia)
Rosanti Rumapea (Universitas Methodist Indonesia)
Indra M. Sarkis S. (Universitas Methodist Indonesia)

TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt

This study focuses on identifying flood-prone areas in North Sumatra, one of the provinces in Indonesia that is very vulnerable to natural disasters such as floods. The impact of floods often results in significant material losses and fatalities. Therefore, it is important to identify high-risk areas so that prevention and mitigation strategies can be implemented effectively. This study uses the K-Means clustering method and Rapid Miner software to determine the area’s most vulnerable to flooding. The results of the analysis show that areas with high flood risk are included in cluster 1, which includes areas with high potential flood vulnerability based on historical data and flood risk indicators that have been analyzed. These findings can be used to design strategies that are implemented and provide greater contributions to the region.