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All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Muhammad Aji Permana
Badan Meteorologi, Klimatologi, dan Geofisika UIN Maulana Malik Ibrahim
Computer Science & IT Mathematics Physics

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PROBABILISTIC SEISMIC HAZARD ANALYSIS IN NORTHERN SUMATERA Permana, Muhammad Aji
Jurnal Neutrino Vol 11, No 1 (2018): October
Publisher : Department of Physics, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (904.767 KB) | DOI: 10.18860/neu.v11i1.5382

Abstract

Northern Sumatera is one of Indonesian region that prone to earthquake. On the other hand, this region has a high level of vulnerability, because of large number of population and high economic growth rate. Thus seismic hazard analysis is needed to analyze earthquake hazard in this region. Probabilistic seismic hazard analysis being carried for count uncertainty factor, ie size, location, and frequency of the earthquake. This method consists of source identification, source characterization, attenuation function selection, and seismic hazard counting. After being calculated, the Northern Sumatera region has hazard value between 0.05 – 1.3 g. The region that has maximum hazard value is the group of islands in western Sumatera, and the minimum one is in the eastern coastline of Sumatera. Banda Aceh and Padang Sidempuan City have a medium level of earthquake hazard, with the most dominant source from Sumatera shear fault. Meulaboh and Gunung Sitoli City have a medium level of earthquake hazard, with the most dominant source from the subduction zone. Medan and Lhoksuemawe City have a low level of earthquake hazard, with the most dominant source from the deep and shallow background.
Uji Performa Prediksi Gempa Bumi di Jawa Timur dengan Artificial Neural Network Muhammad Aji Permana; M Faisal
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 11 Issue 1 June 2023
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/euler.v11i1.19291

Abstract

East Java Province is an area directly adjacent to the Eurasian and Indo-Australian plate subduction zones, this has resulted in East Java province being an area prone to earthquakes. Predictions regarding the frequency of earthquake occurrences are very interesting to study. This needs to be done in order to increase our preparedness in an effort to reduce the risk of earthquakes. Research on earthquake prediction has been carried out, one of which is the artificial neural network method. The purpose of this study is to obtain the best network architecture that is applied to the data on the frequency of earthquake occurrences per month in East Java Province. Data on earthquake occurrences come from the BMKG Nganjuk Geophysics Station, which was recorded during the 2016-2021 period. The data was then grouped into the total frequency of events per month. The criteria for selecting the best network architecture are carried out by comparing each possible architecture's error values. The test method uses SSE (sum square error) criteria for each architectural model of the artificial neural network. The test results show that the input variation has a significant contribution and a greater correlation than the variation in the number of hidden neurons. The best prediction results are obtained in the model with 9-30-1 architecture with an error value of 0.1958.
Co-Authors Muhammad Faisal