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CLUSTERING OF EARTHQUAKE RISK IN INDONESIA USING K-MEDOIDS AND K-MEANS ALGORITHMS

Isna Hidayatur Rifa  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
*Hasih Pratiwi scopus  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
Respatiwulan Respatiwulan  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, Indonesia
Open Access Copyright (c) 2020 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Earthquake is the shaking of the earth's surface due to the shift in the earth's plates. This disaster often happens in Indonesia due to the location of the country on the three largest plates in the world and nine small others which meet at an area to form a complex plate arrangement. An earthquake has several impacts which depend on the magnitude and depth. This research was, therefore, conducted to classify earthquake data in Indonesia based on the magnitudes and depths using one of the data mining techniques which is known as clustering through the application of k-medoids and k-means algorithms. However, k-medoids group data into clusters with medoid as the centroid and it involves using clustering large application (CLARA) algorithm while k-means divide data into k clusters where each object belongs to the cluster with the closest average. The results showed the best clustering for earthquake data in Indonesia based on magnitude and depth is the CLARA algorithm and five clusters were found to have total members of 2231, 1359, 914, 2392, and 199 objects for cluster 1 to cluster 5 respectively.
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Keywords: Earthquake, Data Mining, Clustering; K-Medoids Algorithm; K-Means Algorithm

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