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Thalia Amira Rifda
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Computer Science & IT

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Tingkat Kerawanan Desa Berdasakan Dampak Bencana Kab.Bojonegoro Dengan Metode Clustering Algoritma K-Means Fahrur Rozi, Imam; Muhammad Afif Hendrawan; Thalia Amira Rifda
MULTINETICS Vol. 10 No. 2 (2024): MULTINETICS Nopember (2024)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v10i2.6686

Abstract

The Bojonegoro Regency is a region that is quite prone to disasters, especially floods, landslides, and extreme weather. There have been 2,162 recorded incidents in the past three years, causing significant material and moral losses totaling IDR 1,986,210,000. In an effort to reduce future losses, early prevention measures can be taken by conducting research on the potential disaster-prone areas based on the impact of disasters that have occurred in the region. This study utilized data from 430 villages (2019-2022), considering 6 disaster impact parameters, including the number of disaster events, casualties, affected houses, affected land, material losses, and facility damage. The objective was to identify the vulnerability level of villages using the K-Means Clustering method. The optimal number of clusters was validated using the Davies Bouldin Index (DBI), and the data mining process followed the CRISP-DM standard. The trial results indicated that the optimal number of clusters is k=3. Cluster analysis revealed that Cluster 1 (3 villages) experienced more significant disasters, causing more damage to houses and land; Cluster 2 (17 villages) faced disasters with significant casualties, while Cluster 3 (410 villages) experienced disasters with the lowest impact.
Co-Authors Imam Fahrur Rozi Muhammad Afif Hendrawan