Clustering of Earthquake Prone Areas in Indonesia Using K-Medoids Algorithm
Located right above the ring of fire makes Indonesia prone to natural disasters, especially earthquakes. With the number of earthquakes that have occurred, disaster mitigation is very much needed. The use of data mining methods will certainly help in disaster mitigation. One method that can be used is clustering. The clustering algorithm used in this study is k-Medoids, and comparison with the k-means algorithm is also carried out. The data used are earthquake data from all regions in Indonesia during 2014-2018 that were recorded by the United State Geological Survey (USGS). The results obtained showed that k-medoids giving better silhouette results and computational time than k-means. For the k-medoids cluster results, the highest value of silhouette was 0.4574067 with k = 6. The analysis of each cluster is presented in this paper.
Keywords: clustering,data mining, earthquake, k-medoid.
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