Uji Performa Prediksi Gempa Bumi di Jawa Timur dengan Artificial Neural Network

Muhammad Aji Permana, M Faisal


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.


Prediction;Earthquake;Artificial Neural Network

Full Text:



Pusat Studi Gempa Nasional (Indonesia) and Pusat Penelitian dan Pengembangan Perumahan dan Permukiman (Indonesia), Peta sumber dan bahaya gempa Indonesia tahun 2017. 2017.

S. Sri Lakshmi and R. K. Tiwari, “Model dissection from earthquake time series: A comparative analysis using modern non-linear forecasting and artificial neural network approaches,” Comput Geosci, vol. 35, no. 2, pp. 191–204, Feb. 2009, doi: 10.1016/j.cageo.2007.11.011.

J. Reyes, A. Morales-Esteban, and F. Martínez-Álvarez, “Neural networks to predict earthquakes in Chile,” Applied Soft Computing Journal, vol. 13, no. 2, pp. 1314–1328, 2013, doi: 10.1016/j.asoc.2012.10.014.

I. Kaftan, M. Şalk, and Y. Şenol, “Processing of earthquake catalog data of Western Turkey with artificial neural networks and adaptive neuro-fuzzy inference system,” Arabian Journal of Geosciences, vol. 10, no. 11, Jun. 2017, doi: 10.1007/s12517-017-3021-1.

M. H. Al Banna et al., “Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges,” IEEE Access, vol. 8, pp. 192880–192923, 2020, doi: 10.1109/ACCESS.2020.3029859.

A. Pandit and S. Panda, “Prediction of earthquake magnitude using soft computing techniques: ANN and ANFIS”, doi: 10.1007/s12517-021-07594-2/Published.

T. L. Todelo and C. J. G. Aliac, “Predictability of Earthquake Occurrence Using Auto Regressive Integrated Moving Average (ARIMA) Model,” in International MultiConference of Engineers and Computer Scientists, Hong Kong: IMECS, Mar. 2019.

A. Bhatia, S. Pasari, and A. Mehta, “Earthquake forecasting using artificial neural networks,” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing, Nov. 2018, pp. 823–827. doi: 10.5194/isprs-archives-XLII-5-823-2018.

H. O. Cekim, S. Tekin, and G. Özel, “Prediction of the earthquake magnitude by time series methods along the East Anatolian Fault, Turkey,” Earth Sci Inform, vol. 14, no. 3, pp. 1339–1348, Sep. 2021, doi: 10.1007/s12145-021-00636-z.

A. S. N. Alarifi, N. S. N. Alarifi, and S. Al-Humidan, “Earthquakes magnitude predication using artificial neural network in northern Red Sea area,” J King Saud Univ Sci, vol. 24, no. 4, pp. 301–313, 2012, doi: 10.1016/j.jksus.2011.05.002.

I. M. Murwantara, P. Yugopuspito, and R. Hermawan, “Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 3, pp. 1331–1342, 2020, doi: 10.12928/TELKOMNIKA.v18i3.14756.

M. H. Sultan, “Optimasi Parameter Neural Network Pada Data Time Series untuk Memprediksi Kekuatan Gempa Per Periode,” Jurnal Cauchy, vol. 3, no. 2, pp. 59–71, 2014.

W. Agwil, P. Novianti, and N. Hidayati, “Penerapan Jaringan Saraf Tiruan Pada Data Gempa Bumi di Bengkulu,” Jurnal Statistika, vol. 8, no. 2, pp. 152–158, 2020.

B. P. Yafitra and A. R. Atiqi, “Perbandingan Prediksi Harga Saham Dengan Model ARIMA Dan Artificial Neural Network,” Indonesia Journal on Computing, vol. 4, no. 2, pp. 189–198, 2019, doi: 10.21108/indojc.2019.4.2.344.

F. Günther and S. Fritsch, “neuralnet: Training of Neural Networks,” R J, vol. 2, no. 1, pp. 30–38, Jun. 2010.

DOI: https://doi.org/10.34312/euler.v11i1.19291


  • There are currently no refbacks.

Copyright (c) 2023 Muhammad Aji Permana, M Faisal

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi has been indexed by:


 Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B. J. Habibie, Tilongkabila, Kabupaten Bone Bolango 96554, Gorontalo, Indonesia
 Email: euler@ung.ac.id
 +62-852-55230451 (Call/SMS/WA)
 Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi (p-ISSN: 2087-9393 | e-ISSN:2776-3706) by Department of Mathematics Universitas Negeri Gorontalo is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.  Powered by Public Knowledge Project OJS.