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Mirza Ghulam Rifqi
State Polytechnic of Banyuwangi, East Java, Indonesia
Computer Science & IT

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Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia Mohammad Nur Shodiq; Dedy Hidayat Kusuma; Mirza Ghulam Rifqi; Ali Ridho Barakbah; Tri Harsono
JOIV : International Journal on Informatics Visualization Vol 2, No 1 (2018)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1115.541 KB) | DOI: 10.30630/joiv.2.1.106

Abstract

A model of artificial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey’s (USGS). Six clusters was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter’s scale magnitude.
Co-Authors Ali Ridho Barakbah Kusuma, Dedy Hidayat Mohammad Nur Shodiq Tri Harsono