Clustering zonasi daerah rawan bencana alam Provinsi Jawa Tengah menggunakan algoritma k-means dan library geopandas
DOI:
https://doi.org/10.24246/itexplore.v4i1.2025.pp116-127Keywords:
Flood, Landslide, K-Means, Disaster MitigationAbstract
Based on the 2016-2020 Central Java Disaster Risk Assessment, floods and landslides are the most frequent disasters, with 818 flood cases accounting for 31.33% of the total disasters and landslides accounting for 29.57%. This study aims to cluster disaster-prone areas in Central Java using the K-Means algorithm and the GeoPandas library. Data on disaster events for the period 2019-2023 was obtained from the National Disaster Management Agency, while administrative map data of Central Java was downloaded from the Geoportal of Central Java Province. The research stages include data collection, data cleaning, standardization using the Standard Scaler method, application of the K-Means algorithm for regional clustering, and visualization of results using GeoPandas. The results showed that Central Java was divided into four clusters, namely: cluster 0 (disaster-prone areas) includes 3 regions, cluster 1 (non-disaster-prone areas) has 22 regions, cluster 2 (flood-prone areas) consists of 7 regions, and cluster 3 (landslide-prone areas) has 3 regions. The results of this research provide spatial data-based information that can be used as a basis in decision-making for disaster mitigation in Central Java.
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