Earthquake Distribution Mapping in Indonesia Using K-Means Clustering Algorithm
Deni Malik, I Made Artha Agastya, Andreo Yustiantoro Anjaya, Kusrini Kusrini, Sebastianus Hartantyo · 2024
Earthquakes are natural disasters that can quickly destroy existing infrastructure. For urban planning, it is essential to analyze and mitigate potential earthquakes. This research aims to map the distribution of earthquake points in Indonesia using the K-Means Clustering algorithm, using data from the Meteorology, Climatology, and Geophysics Agency (BMKG) for the period 2008–2023. The K-Means Clustering algorithm is applied to group earthquake data based on depth, magnitude, and their combinations. The clustering results are analyzed using the Silhouette Score to determine the optimal number of clusters. The two clusters scenario achieved the highest Silhouette Score, indicating effective separation of earthquake data based on the analyzed characteristics. This study confirms that using KMeans Clustering can provide deep insights into seismic patterns in Indonesia and aid in identifying active seismic zones, which is crucial for disaster mitigation.