Estimation of Flores Sea Aftershock Rupture Data Based on AI

Adi Jufriansah; Azmi Khusnani; Yudhiakto Pramudya; Mulya Afriyanto

doi:10.12928/irip.v6i1.6705

Authors

Adi Jufriansah IKIP Muhammadiyah Maumere https://orcid.org/0000-0003-0659-8093
Azmi Khusnani IKIP Muhammadiyah Maumere https://orcid.org/0000-0001-6287-4246
Yudhiakto Pramudya Universitas Ahmad Dahlan
Mulya Afriyanto Meteorological, Climatological, and Geophysical Agency (BMKG)

DOI:

https://doi.org/10.12928/irip.v6i1.6705

Keywords:

Aftershock, Flores Sea Earthquake, Machine Learning, Classification Algorithm, Regression Algorithm

Abstract

The earthquake catalog notes that there have been earthquakes with Mw > 7 that hit the Flores area, three of which occurred in the Flores Sea in 1992, 2015, and 2021. Revealed that the seismic activity of Eastern Indonesia is thought to be influenced by the isolated thrust fault segment of the island of Flores and the island of Wetar. The study of the rising fault segment on Flores Island and Wetar Island helps in further understanding the fault behavior, earthquake pattern, and seismic risk in the Flores Sea region. In earthquakes with giant magneto, an aftershock can occur due to the interaction of ground movements. This research analyzes and compares the data from the evaluation of the classification algorithm and the regression algorithm. The initial stages of this research include requesting IRIS DMC Web Service data. The data is then subjected to a cleaning process to obtain the expected feature extraction. The next stage is to perform the clustering process. This stage is carried out to label dependent data by adding new features as data clusters. The following procedure divides the validation value, which consists of training and test data. The estimation results show that the classification algorithm's evaluation value is better than that of the regression algorithm. The evaluation value of several algorithms indicates this, with an accuracy rate between 80% and 100%.

Author Biographies

Adi Jufriansah, IKIP Muhammadiyah Maumere

Mathematics Education Department, IKIP Muhammadiyah Maumere, Indonesia

Azmi Khusnani, IKIP Muhammadiyah Maumere

Physics Education Department, IKIP Muhammadiyah Maumere, Indonesia

Yudhiakto Pramudya, Universitas Ahmad Dahlan

Postgraduate Program of Physics Education, Faculty of Teacher Training and Education, Universitas Ahmad Dahlan, Indonesia

Mulya Afriyanto, Meteorological, Climatological, and Geophysical Agency (BMKG)

Meteorological, Climatological, and Geophysical Agency (BMKG), Sikka Regency, Indonesia

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