Prediksi Tsunami Pada Gempa Menggunakan Random Forest Classifier
Keywords:
gempa, machine learning, random forest, tsunami
Abstract
Gempa yang diikuti oleh tsunami memiliki ciri khusus seperti kedalaman, besar, dan lokasi tertentu yang harus dianalisis terlebih dahulu sebelum dinyatakan akan diikuti tsunami atau tidak. Kemajuan teknologi machine learning memungkinkan kita melakukan prediksi terjadinya tsunami lebih efisien dibanding sebelumnya. Riset ini memanfaatkan machine learning khususnya algoritme Random Forest Classifier untuk membuat model yang dapat memprediksi potensi tsunami dengan
menggunakan data historis global significant earthquake milik NOAA dari tahun 2100 SM yang berisi pola Negara, Kode Region, Lintang, Bujur, Tahun, Bulan, Tanggal, Kedalaman, serta Besarnya gempa. Hasil simulasi model untuk memprediksi tsunami dari gempa pada testing set menunjukan akurasi di atas 75%.
References
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[10] K. J. Grimm, G. L. Mazza, P. Davoudzadeh, K. J. Grimm, G. L. Mazza, and P. Davoudzadeh, “Model Selection in Finite Mixture Models: A k-Fold Cross-Validation Approach Model Selection in Finite Mixture Models: A k - Fold Cross-Validation Approach,”Struct. Equ. Model. A Multidiscip. J., vol. 00, no. 00, pp. 1–11, 2016.
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[12] X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Inf. Sci. (Ny)., vol. 340–341, pp. 250–261, 2016.
[13] M. Buda, A. Maki, and M. A. Mazurowski, “A systematic study of the class imbalance problem in convolutional neural networks,”Neural Networks, vol. 106, pp.249-259, 2018.
[14] P. A. Flach and M. Kull, “Precision-Recall-Gain curves: PR analysis done right,” Adv. Neural Inf. Process. Syst., vol. 2015-January, pp. 838–846, 2015.
[15] D. J. DIttman, T. M. Khoshgoftaar, and A. Napolitano, “The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data,” Proc. - 2015 IEEE 16th Int. Conf. Inf. Reuse Integr. IRI 2015, pp. 457–463, 2015.
Published
2020-03-16
How to Cite
Lasama, J., E.P., A., & Prasetiadi, A. (2020). Prediksi Tsunami Pada Gempa Menggunakan Random Forest Classifier. Proceedings of the National Conference on Electrical Engineering, Informatics, Industrial Technology, and Creative Media, 2(1), 39-47. Retrieved from https://conferences.ittelkom-pwt.ac.id/index.php/centive/article/view/85
