Analisis Gempa Bumi Di Indonesia Dengan Metode Clustering

Arji Prasetio; M. Makmun  Effendi; M. Najamuddin  Dwi M

doi:10.47065/bit.v4i3.820

Arji Prasetio * Univesitas Pelita Bangsa, Indonesia
M. Makmun Effendi Universitas Pelita Bangsa, Indonesia
M. Najamuddin Dwi M Universitas Pelita Bangsa, Indonesia

DOI: https://doi.org/10.47065/bit.v4i3.820

Keywords: Data Mining, K-Means, Gempa Bumi, indonesia, Clustering,

Abstract

Indonesia is known as an archipelagic country because it consists of thousands of islands stretching from Sabang in the west to Merauke in the east. Testing earthquake data using the K-Means algorithm, where the results also show a new insight, namely the grouping of earthquake-prone areas in Indonesia based on 3 clusters. Cluster 1 is a category of areas with a relatively low level of earthquake-prone areas in Indonesia, namely 209 out of 1113 categories of the number of cases based on the area tested, then cluster 2 is a category of areas with a moderate level of earthquake-prone areas in Indonesia, namely 863 out of 1113 the category of the number of cases based on the area tested, and finally cluster 3 is the category of area with a high level of earthquake-prone areas in Indonesia, namely 41 out of 1113 categories of the number of cases based on the area tested. Tests using the earthquake clustering method with the K-Means algorithm can produce clusters that have cluster group members according to manual calculations such as Cluster_0 in Rapid Miner has 209 cluster members representing the Low cluster, Cluster_1 has 863 cluster group members representing the Medium cluster, and Cluster_2 has 41 cluster members corresponding to the cluster representation High.

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