Prediksi Kerusakan Bangunan Pasca Gempa Bumi Menggunakan Metode Deep Neural Network

Authors

  • Fakhrurrozi Fakhrurrozi Universitas Dian Nuswantoro
  • Danny Oka Ratmana Universitas Dian Nuswantoro
  • Nurul Anisa Sri Winarsih Universitas Dian Nuswantoro
  • Galuh Wilujeng Saraswati Universitas Dian Nuswantoro
  • Muhammad Syaifur Rohman Universitas Dian Nuswantoro
  • Filmada Ocky Saputra Universitas Dian Nuswantoro
  • Ricardus Anggi Pramunendar Universitas Dian Nuswantoro
  • Guruh Fajar Shidik Universitas Dian Nuswantoro

DOI:

https://doi.org/10.32493/jtsi.v7i1.37181

Keywords:

Deep Neural Network; Building Damage Prediction; Earthquake; Learning Rate; Optimizer

Abstract

Addressing the challenge of predicting earthquake-induced building damage, this study proposes the innovative use of Deep Neural Networks (DNN) as a solution. Focusing on optimizing predictive models, the research evaluates the effectiveness of various optimizers - ADAM, SGD, RMSprop, and Adagrad - coupled with adjustments in the learning rate to determine the most efficient configuration. The experiment was conducted to compare the performance of each optimizer in predicting post-earthquake building damage, a critical issue in disaster mitigation. The results demonstrate that ADAM significantly outperforms other optimizers, achieving the highest accuracy of up to 90.50% at a learning rate of 0.001, with RMSprop as its closest competitor. While SGD and Adagrad yielded lower accuracies, SGD showed improvement with higher learning rates. The variance analysis confirmed that the choice of optimizer significantly impacts model performance, with the p-value indicating strong statistical significance for optimizers (1.23E-09), whereas the learning rate had no significant impact (p-value 0.56098964). These findings underline the importance of selecting the appropriate optimizer to enhance the accuracy of DNN models for building damage prediction, a crucial aspect in emergency response planning and earthquake disaster mitigation efforts. This research contributes significantly to the development of more accurate predictive models, which are essential in minimizing the risks of earthquake disasters.

Author Biography

Fakhrurrozi Fakhrurrozi, Universitas Dian Nuswantoro

Fakultas Ilmu Komputer, Program Studi Teknik Informatika, Universitas Dian Nuswantoro; Pusat Kajian Intelligent Distributed and Surveillance System (IDSS)

References

Caelen, O. (2017). A Bayesian interpretation of the confusion matrix. September, 429–450. https://doi.org/10.1007/s10472-017-9564-8

Chaurasia, K., Kanse, S., Yewale, A., Singh, V. K., Sharma, B., & Dattu, B. R. (2019). Predicting Damage to Buildings Caused by Earthquakes Using Machine Learning Techniques. Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing, IACC 2019, December, 81–86. https://doi.org/10.1109/IACC48062.2019.8971453

Da Poian, V., Theiling, B., Clough, L., McKinney, B., Major, J., Chen, J., & Hörst, S. (2023). Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry. Frontiers in Astronomy and Space Sciences, 10(May), 1–17. https://doi.org/10.3389/fspas.2023.1134141

Das, L., Sivaram, A., & Venkatasubramanian, V. (2020). Hidden Representations in Deep Neural Networks : Part 2 . Regression Problems. Computers and Chemical Engineering, 106895. https://doi.org/10.1016/j.compchemeng.2020.106895

Firmansyah, Rusmal, Shidiq, G. F. (2023). Peningkatan Deep Neural Network pada Kasus Prediksi Diabetes Menggunakan PSO. 22(4), 882–892.

Ghenescu, V., Barnoviciu, E., Carata, S., Ghenescu, M., Mihaescu, R., & Chindea, M. (n.d.). Object Recognition on Long Range Thermal Image Using State of the Art DNN. 1–4.

Ghimire, S., Guéguen, P., Giffard-Roisin, S., & Schorlemmer, D. (2022). Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake. Earthquake Spectra, 38(4), 2970–2993. https://doi.org/10.1177/87552930221106495

Hu, H., Lei, T., Hu, J., Zhang, S., & Kavan, P. (2018). Disaster-mitigating and general innovative responses to climate disasters: Evidence from modern and historical China. International Journal of Disaster Risk Reduction, 28(August 2017), 664–673. https://doi.org/10.1016/j.ijdrr.2018.01.022

Isnaeni, A. Y., & Prasetyo, S. Y. J. (2022). Klasifikasi Wilayah Potensi Risiko Kerusakan Lahan Akibat Bencana Tsunami Menggunakan Machine Learning. Jurnal Teknik Informatika Dan Sistem Informasi, 8(1), 33–42. https://doi.org/10.28932/jutisi.v8i1.4056

Maryani, E. (2021). The role of education and geography on disaster preparedness. IOP Conference Series: Earth and Environmental Science, 683(1). https://doi.org/10.1088/1755-1315/683/1/012043

Matchev, K. T., Matcheva, K., & Roman, A. (2022). Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra. Planetary Science Journal, 3(9), 205. https://doi.org/10.3847/PSJ/ac880b

Maulana, F. F., & Rochmawati, N. (2020). Klasifikasi Citra Buah Menggunakan Convolutional Neural Network. Journal of Informatics and Computer Science (JINACS), 1(02), 104–108. https://doi.org/10.26740/jinacs.v1n02.p104-108

Mittal, V. (2020). Exploring The Dimension of DNN Techniques For Text Categorization Using NLP.

Randles, B. M., Pasquetto, I. V., Golshan, M. S., & Borgman, C. L. (2017). Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, 17–18. https://doi.org/10.1109/JCDL.2017.7991618

Retson, T. A., Besser, A. H., Sall, S., Golden, D., & Hsiao, A. (2019). Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging. 34(3), 192–201. https://doi.org/10.1097/RTI.0000000000000385

Shah, D., & Campbell, W. (2018). A Comparative Study of LSTM and DNN for Stock Market Forecasting. 2018 IEEE International Conference on Big Data (Big Data), 4148–4155. https://doi.org/10.1109/BigData.2018.8622462

Su, Y., Rong, G., Ma, Y., Chi, J., & Liu, X. (2022). Hazard Assessment of Earthquake Disaster Chains Based on Deep Learning — A Case Study of Mao County , Sichuan Province. 9(May), 1–10. https://doi.org/10.3389/feart.2021.683903

Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Review Article Data Processing and Text Mining Technologies on Electronic Medical Records : A Review. 2018.

Triastari, I., Dwiningrum, S. I. A., & Rahmia, S. H. (2021). Developing Disaster Mitigation Education with Local Wisdom: Exemplified in Indonesia Schools. IOP Conference Series: Earth and Environmental Science, 884(1). https://doi.org/10.1088/1755-1315/884/1/012004

Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 Januari 2024

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Published

2024-01-30

How to Cite

Fakhrurrozi, F., Ratmana, D. O., Winarsih, N. A. S., Saraswati, G. W., Rohman, M. S., Saputra, F. O., … Shidik, G. F. (2024). Prediksi Kerusakan Bangunan Pasca Gempa Bumi Menggunakan Metode Deep Neural Network. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(1), 131–142. https://doi.org/10.32493/jtsi.v7i1.37181

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Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

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Article

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Copyright (c) 2024 Fakhrurrozi Fakhrurrozi, Danny Oka Ratmana, Nurul Anisa Sri Winarsih, Galuh Wilujeng Saraswati, Muhammad Syaifur Rohman, Filmada Ocky Saputra, Ricardus Anggi Pramunendar, Guruh Fajar Shidik

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