Oman Somantri, Santi Purwaningrum, Riyanto Riyanto


Earthquakes are a type of natural disaster that currently cannot be predicted. Predicting the value of earthquake magnitude for related parties such as government and National Disaster Management Authority is very important. Furthermore, the results of earthquake predictions by several parties are used as indicators in post-earthquake response in minimizing the risks that will occur. Several studies have applied machine learning methods to predict earthquakes such as deep neural networks and parallel Support Vector Regression. In this article, we propose a data mining method using the Support Vector Machine (SVM) algorithm accompanied by the optimization of the windowing parameter value in the model that is applied to predict the value of the earthquake magnitude. Based on its advantages, the SVM model was chosen because it has been applicable in time series data processing. In the experimental stage process, parameter settings are first carried out, namely setting the kernel type, sampling type, and number of windowing to optimize the level of accuracy of the resulting model. The results showed that the best model with the smallest Root Mean Square Error (RMSE) was 0.712.

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BMKG. (2021). Data Online - Pusata Database - BMKG.

Cecilia Charlene, S. S. H. (2020). Analisis Prediksi Dan Anomali Gempabumi Menggunakan Machine Learning [Institut Teknologi Telkom Purwokerto].

Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250.

Fatimatuzzahra, F., Didik, L. A., & Bahtiar, B. (2020). Analisis Periodisitas Gempa Bumi Diwilayah Kabupaten Lombok Barat Dengan Menggunakan Metode Statistik Dan Transformasi Wavelet. Jurnal Fisika dan Aplikasinya, 16(1), 33.

Ginting, N. Y. I., Novianty, A., & Prasasti, M. T. A. L. (2020). Estimasi Magnitudo Gempa Bumi Dari Sinyal Seismik Gelombang P Menggunakan Metode Regresi Polinomial. eProceedings of Engineering, 7(2).

Jena, R., Pradhan, B., Beydoun, G., Alamri, A. M., Ardiansyah, Nizamuddin, & Sofyan, H. (2020). Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia. Science of the Total Environment, 749, 141582.

Kollam, M., & Joshi, A. (2020). Earthquake Forecasting by Parallel Support Vector Regression Using CUDA. Proceedings - 2020 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2020, 150–155.

Mallouhy, R., Jaoude, C. A., Guyeux, C., & Makhoul, A. (2019, Desember 1). Major earthquake event prediction using various machine learning algorithms. 6th International Conference on Information and Communication Technologies for Disaster Management, ICT-DM 2019.

Moraes, R., Valiati, J. F., & Gavião Neto, W. P. (2013). Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 40(2), 621–633.

Noor, A. (2018). Perbandingan Algoritma Support Vector Machine Biasa dan Support Vector Machine berbasis Particle Swarm Optimization untuk Prediksi Gempa Bumi. Jurnal Humaniora Teknologi, 4(1).

Somantri, O., & Apriliani, D. (2018). Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal. Jurnal Teknologi Informasi dan Ilmu Komputer, 5(5), 537.

Tomar, D., & Agarwal, S. (2015). Twin Support Vector Machine: A review from 2007 to 2014. In Egyptian Informatics Journal (Vol. 16, Nomor 1, hal. 55–69). Elsevier B.V.

Utomo, D. P., & Purba, B. (2019). Penerapan Datamining pada Data Gempa Bumi Terhadap Potensi Tsunami di Indonesia. Prosiding Seminar Nasional Riset Information Science (SENARIS), 1, 846.

Wei, H., Wang, M., Song, B., Wang, X., & Chen, D. (2018). Study on the magnitude of reservoir-triggered earthquake based on support vector machines. Complexity, 2018.

Wyss, M. (2014). Earthquake Hazard, Risk and Disasters. In Earthquake Hazard, Risk and Disasters. Elsevier Inc.

Xiong, P., Tong, L., Zhang, K., Shen, X., Battiston, R., Ouzounov, D., Iuppa, R., Crookes, D., Long, C., & Zhou, H. (2021). Towards advancing the earthquake forecasting by machine learning of satellite data. Science of the Total Environment, 771, 145256.

Xu, C., Dai, F., Xu, X., & Lee, Y. H. (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145–146, 70–80.

Yang, D. H., Zhou, X., Wang, X. Y., & Huang, J. P. (2021). Mirco-earthquake source depth detection using machine learning techniques. Information Sciences, 544, 325–342.

Yousefzadeh, M., Hosseini, S. A., & Farnaghi, M. (2021). Spatiotemporally explicit earthquake prediction using deep neural network. Soil Dynamics and Earthquake Engineering, 144, 106663.



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