Classification of Potential Tsunami Disaster Due to Earthquakes in Indonesia Based on Machine Learning
Main Article Content
Abstract
Earthquakes and tsunamis pose significant threats to Indonesia due to its unique geological positioning at the convergence of four tectonic plates. This study focuses on classifying the potential occurrence of tsunami disasters following earthquakes using various data mining methods, including k-Nearest Neighbor (kNN), Naïve Bayes, Decision Tree and Ensemble Method, and Linear Regression. The research employs a qualitative approach to systematically understand and describe the context of natural disasters, utilizing both primary and secondary data collection techniques. Performance evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, and Recall are utilized to assess the effectiveness of each method in predicting potential tsunami events. The findings reveal that the kNN method exhibits the highest performance, with an AUC of 94.4% and a precision of 82.8%, indicating robust predictive capabilities. However, misclassifications were observed, emphasizing the need for further refinement. Naïve Bayes also shows promising results with an AUC of 84.5% and precision of 78.6%. Decision Tree and Ensemble Method models, such as Random Forest and AdaBoost, demonstrate reasonable performance, with Random Forest achieving the highest AUC of 71.9%. Linear Regression is employed to explore the correlation between earthquake attributes and tsunami occurrence, revealing a weak relationship. Further research integrating advanced modeling approaches and additional earthquake attributes is recommended to enhance the predictive capabilities of tsunami risk assessment models. The study underscores the importance of employing diverse machine learning techniques and evaluating their performance metrics to refine the accuracy of tsunami prediction models, ultimately contributing to practical disaster preparedness and mitigation strategies.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Opilah, B. S., Karyadi, B., Johan, H., & Mayub, A. (2023). Model Integrasi Mitigasi Bencana Gempa Bumi pada Konsep Gelombang. Jurnal Pendidikan Fisika, 11(1), 28-39. DOI: http://dx.doi.org/10.24127/jpf.v11i1.6754.
Syafitri, Y., Bahtiar, B., & Didik, L. A. (2019). Analisis pergeseran lempeng bumi yang meningkatkan potensi terjadinya gempa bumi di pulau Lombok. Konstan-Jurnal Fisika Dan Pendidikan Fisika, 4(2), 139-146. DOI: https://doi.org/10.20414/konstan.v4i2.43.
Hadi, H., Agustina, S., & Subhani, A. (2019). Penguatan kesiapsiagaan stakeholder dalam pengurangan risiko bencana alam gempabumi. Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi, 3(1), 30-40. DOI: https://doi.org/10.29408/geodika.v3i1.1476.
Deswita, D., Yuliharni, S., & Efniyati, N. N. (2023). STUDI KASUS: GAMBARAN KESIAPSIAGAAN REMAJA MENGHADAPI GEMPA BUMI DAN TSUNAMI. Jurnal'Aisyiyah Medika, 8(2). DOI: https://doi.org/10.36729/jam.v8i2.1112.
Puspitasari, L., Prastistho, B., & Prasetya, J. D. (2023). MODEL KESIAPSIAGAAN KELUARGA TERHADAP ANCAMAN BAHAYA BENCANA GEMPA BUMI DESA CONDONGCATUR, KAPANEWON DEPOK, KABUPATEN SLEMAN, DI YOGYAKARTA. Indonesian Journal of Environment and Disaster, 2(1), 1-9. DOI: https://doi.org/10.20961/ijed.v2i1.479
Prathivi, R. (2020). Optimasi Algoritme Naive Bayes Untuk Klasifikasi Data Gempa Bumi di Indonesia Berdasarkan Hiposentrum. Telematika, 13(1), 36-43.
Marutho, D. (2019). Perbandingan Metode Naive Bayes, KNN, Decision Tree Pada Laporan Water Level Jakarta. Jurnal Ilmiah Infokam, 15(2). DOI: https://doi.org/10.53845/infokam.v15i2.175.
Giri, G. A. V. M. (2018). Klasifikasi Musik Berdasarkan Genre dengan Metode K-Nearest Neighbor. Jurnal Ilmu Komputer, 11(2), 104-108.
Rahmansyah, N., Muliyani, D., Mardiani, E., & Rahman, A. (2022). Perancangan Sistem Transaksi Berbasis Web pada UKM Pangkas Rambut Tasik. Jurnal Sistem Informasi Bisnis (JUNSIBI), 3(1), 22-31. DOI: https://doi.org/10.55122/junsibi.v3i1.412
Mardiani, E., Rahmansyah, N., & Kurniati, I. (2023). Website Design at SDN Cipete Utara 07. SITEKIN: Jurnal Sains, Teknologi Dan Industri, 20(2), 891-898. DOI: http://dx.doi.org/10.24014/sitekin.v20i2.22438.
Mardiani, E., & Ramadhan, F. A. (2023). Design Information System Sales of Nuts and Bolts at PT. Catur Naga Steelindo. SITEKIN: Jurnal Sains, Teknologi dan Industri, 20(2), 729-735. DOI: http://dx.doi.org/10.24014/sitekin.v20i2.21948.
Matondang, N., Mardiani, E., Wahyudi, P., & Saebani, A. (2019). Aplikasi Komputer. Jakarta: Mitra Wacana Media.
Indriyawati, H., & Khoirudin. (2019). Penerapan Metode Regresi Linier dalam Koherensi Pengolahan Data Bahan Baku Tiandra Store Guna Meningkatkan Mutu Produksi. Sintak, 3(2).
Pratama, F. H., Triayudi, A., & Mardiani, E. (2022). Data mining k-medoids dan k-means untuk pengelompokan potensi produksi kelapa sawit di indonesia. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 7(4), 1294-1310.
Hozairi, H., Anwari, A., & Alim, S. (2021). Implementasi Orange Data Mining Untuk Klasifikasi Kelulusan Mahasiswa Dengan Model K-Nearest Neighbor, Decision Tree Serta Naive Bayes. Netw. Eng. Res. Oper, 6(2), 133.
Djamaludin, M. A., Triayudi, A., & Mardiani, E. (2022). Analisis Sentimen Tweet KRI Nanggala 402 di Twitter menggunakan Metode Naïve Bayes Classifier. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(2), 161–166. https://doi.org/10.35870/jtik.v6i2.398.
Mardiani, E., Rahmansyah, N., Ningsih, S., Lantana, D. A., Wirawan, A. S. P., Wijaya, S. A., & Putri, D. N. (2023). Komparasi Metode Knn, Naive Bayes, Decision Tree, Ensemble, Linear Regression Terhadap Analisis Performa Pelajar Sma. Innovative: Journal Of Social Science Research, 3(2), 13880-13892. DOI: https://doi.org/10.31004/innovative.v3i2.1949.
Indriyanti, I., Ichsan, N., Fatah, H., Wahyuni, T., & Ermawati, E. (2022). Implementasi Orange Data Mining Untuk Prediksi Harga Bitcoin. Jurnal Responsif: Riset Sains dan Informatika, 4(2), 118-125. https://doi.org/10.51977/jti.v4i2.762.
Basuki, B., & Rejeki, S. (2021). PENDEKATAN DAN METODA PENELITIAN FENOMENA GEMPA BUMI. Jurnal Teknik Sipil Giratory UPGRIS, 2(2), 52-65. DOI: https://doi.org/10.26877/giratory.v2i2.10343.
Sarofi, M. A. A., Irhamah, I., & Mukarromah, A. (2020). Identifikasi Genre Musik dengan Menggunakan Metode Random Forest. Jurnal Sains dan Seni ITS, 9(1), D79-D86. http://dx.doi.org/10.12962/j23373520.v9i1.51311.
Rim, D., Baraldi, R., Liu, C. M., LeVeque, R. J., & Terada, K. (2022). Tsunami early warning from global navigation satellite system data using convolutional neural networks. Geophysical Research Letters, 49(20). https://doi.org/10.1029/2022gl099511
Mulia, I. E., Ueda, N., Miyoshi, T., Gusman, A. R., & Satake, K. (2022). Machine learning-based tsunami inundation prediction derived from offshore observations. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-33253-5
Mazinani, I., Ismail, Z., Shamshirband, S., Hashim, A. M., Mansourvar, M., & Zalnezhad, E. (2016). Estimation of tsunami bore forces on a coastal bridge using an extreme learning machine. Entropy, 18(5), 167. https://doi.org/10.3390/e18050167.