Clustering of Post-Disaster Building Damage Levels Using Discrete Wavelet Transform and Principal Component Analysis

DOI: https://doi.org/10.30996/jitcs.12270
Keywords: clustering of building damage, Discrete Wavelet Transform, post-disaster building damage assessment, Principal Component Analysis

Abstract

Damage assessment of buildings after natural disasters is generally performed manually by a team of experts at the disaster site, making it prone to human error and resulting in low accuracy in classifying the level of damage. This research aims to develop a more efficient and accurate method in post-disaster building damage assessment by integrating Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) techniques. The main contribution of this research is the use of DWT as well as the application of this method on more than one image to improve the accuracy of damage level classification. A total of nine unlabelled images of post-disaster buildings were used in this study, which were obtained from the Regional Disaster Management Agency or Badan Penanggulangan Bencana Daerah (BPBD) of Malang City, Indonesia. The methods applied include data pre-processing, DWT decomposition for image analysis to identify features, and clustering using PCA to cluster the level of building damage into light, medium, and heavy categories, which are then evaluated based on accuracy. The results showed that the method yielded 100% accuracy with validation results from surveyors, as evidenced through 2D and 3D visualisations based on principal components (PC1-PC3). These findings confirm that the integration of DWT and PCA can be an effective alternative in improving the accuracy of post-disaster building damage assessment, as well as supporting decision-making in rehabilitation and reconstruction after natural disasters.

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Author Biographies

Putri Purnamasari, Universitas Islam Negeri Maulana Malik Ibrahim

Department of Informatics Engineering

Mochamad Imamudin, Universitas Islam Negeri Maulana Malik Ibrahim

Department of Informatics Engineering

Syahiduz Zaman, Universitas Islam Negeri Maulana Malik Ibrahim

Department of Informatics Engineering

A’la Syauqi, Universitas Islam Negeri Maulana Malik Ibrahim

Department of Informatics Engineering

Agung Teguh Wibowo Almais, Universitas Islam Negeri Maulana Malik Ibrahim

Department of Electrical Engineering

References

Ahn, J., Ji, S.-H., Ahn, S. J., Park, M., Lee, H.-S., Kwon, N., . . . Kim, Y. (2020). Performance evaluation of normalization-based CBR models for improving construction cost estimation. Automation in Construction, 119. doi:https://doi.org/10.1016/j.autcon.2020.103329

Almais, A. T., Susilo, A., Naba, A., Sarosa, M., Crysdian, C., Tazi, I., . . . Hendro, W. (2023). Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters. IEEE Access, 11, 74590 - 74601. doi:https://doi.org/10.1109/ACCESS.2023.3275852

Almais, A. T., Susilo, A., Naba, A., Sarosa, M., Juwono, A. M., Crysdian, C., . . . Wicaksono, H. (2024). Characterization of Structural Building Damage in Post-Disaster Using GLCM-PCA Analysis Integration. IEEE Access, 12, 146190-146201. doi:https://doi.org/10.1109/ACCESS.2024.3469637

Amri, M. R., Yulianti, G., Yunus, R., Wiguna, S., Adi, A. W., Ichwana, A. N., . . . Septian, R. T. (2016). Risiko Bencana Indonesia (RBI). Jakarta, Indonesia: Badan Nasional Penanggulangan Bencana. Retrieved from https://inarisk.bnpb.go.id/pdf/Buku%20RBI_Final_low.pdf

Biswal, P. R., Sanyal, B., & Mohapatra, R. K. (2022). Building Damage Detection Using Discrete Wavelet Transforms and Convolutional Neural Networks. Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. 2nd, pp. 743–753. Jaipur, India: Springer, Singapore. doi:https://doi.org/10.1007/978-981-16-6332-1_60

Christaki, M., Vasilakos, C., Papadopoulou, E.-E., Tataris, G., Siarkos, I., & Soulakellis, N. (2022). Building Change Detection Based on a Gray-Level Co-Occurrence Matrix and Artificial Neural Networks. Drones, 6(12), 414. doi:https://doi.org/10.3390/drones6120414

Coburn, A. W., Spence, R. J., & Pomonis, A. (1994). Vulnerability and Risk Assessment: Disaster Management Training Programme (2nd ed.). Cambridge, United Kingdom: UNDP Disaster Management Training Programme. Retrieved from https://books.google.co.id/books/about/Vulnerability_and_Risk_Assessment.html?id=zS24mQEACAAJ

Fu'adah, A. F., Almais, A. T., & Syauqi, A. (2023). Klasifikasi Tingkat Kerusakan Sektor Pasca Bencana Alam Menggunakan Metode MULTIMOORA Berbasis Web. JISKA (Jurnal Informatika Sunan Kalijaga), 8(1), 222-230. doi:https://doi.org/10.14421/jiska.2023.8.3.222-230

Hadiguna, R. A., Kamil, I., Delati, A., & Reed, R. (2014). Implementing a web-based decision support system for disaster logistics: A case study of an evacuation location assessment for Indonesia. International Journal of Disaster Risk Reduction, 9, 38-47. doi:https://doi.org/10.1016/j.ijdrr.2014.02.004

Huda, F. I., Karyatanti, I. D., & Dewantara, B. Y. (2019). Identifikasi Gangguan Belitan Stator Motor Induski Metode Wavelet. Prosiding Seminar Nasional Sains dan Teknologi. 10. Semarang, Indonesia: Fakultas Teknik Universitas Wahid Hasyim. doi:https://doi.org/10.36499/psnst.v1i1.2901

Jolliffe, I. T. (2002). Principal Component Analysis. New York, United States: Springer. doi:https://doi.org/10.1007/b98835

Junaidi, M. (2019). Sistem pendukung keputusan untuk rehabilitasi dan rekonstruksi fisik aksi setelah bencana alam menggunakan metode wp-topsis. Malang, Indonesia: Universitas Islam Negeri Maulana Malik Ibrahim. Retrieved from http://etheses.uin-malang.ac.id/id/eprint/16610

Khairuna, K., Sriani, S., & Triase, T. (2017). Pendekomposisian Citra Digital dengan Algoritma DWT. Algoritma: Jurnal Ilmu Komputer dan Informatika, 1(1). Retrieved from https://moraref.kemenag.go.id/documents/article/98021043410574412

Krichen, M., Abdalzaher, M. S., Elwekeil, M., & Fouda, M. M. (2024). Managing natural disasters: An analysis of technological advancements, opportunities, and challenges. Internet of Things and Cyber-Physical Systems, 4, 99-109. doi:https://doi.org/10.1016/j.iotcps.2023.09.002

Molin, S. (2021). Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization. Birmingham, UK: Packt Publishing.

Putra, I. S., Hermawan, F., & Hatmoko, J. U. (2020). Penilaian Kerusakan dan Kerugian Infrastruktur Publik Akibat Dampak Bencana Banjir di Kota Semarang. Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil, 25(2), 86-97. doi:http://dx.doi.org/10.32497/wahanats.v25i2.2154

Rosyida, A., Maulani, M. A., Firmansyah, Y., Setiawan, T., Pangesti, K. P., & Ichsan, F. K. (2024). Data Bencana Indonesia Tahun 2023 (Vol. 3). Jakarta, Indonesia: Pusdatinkom BNPB. Retrieved from https://bnpb.go.id/buku/buku-data-bencana-indonesia-tahun-2023

Susilo, V., Isnanto, R. R., & Riyadi, M. A. (2020). Herbal Leaf Pattern Analisis Using Principal Component Analisis (PCA) and Canberra Distance. 2020 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). 7, pp. 100-104. Semarang, Indonesia: IEEE. doi:https://doi.org/10.1109/ICITACEE50144.2020.9239235

Tazi, I., Isnaini, N. L., Mutmainnah, M., & Ainur, A. (2019). Principal Component Analysis (PCA) Method for Classification of Beef and Pork Aroma Based on Electronic Nose. Indonesian Journal of Halal Research, 1(1), 5-8. doi:https://doi.org/10.15575/ijhar.v1i1.4155

Troccoli, E. B., Cerqueira, A. G., Lemos, J. B., & Holz, M. (2022). K-means clustering using principal component analysis to automate label organization in multi-attribute seismic facies analysis. Journal of Applied Geophysics, 198. doi:https://doi.org/10.1016/j.jappgeo.2022.104555

Published
2025-01-28
How to Cite
Purnamasari, P., Imamudin, M., Zaman, S., Syauqi, A., & Almais, A. T. W. (2025). Clustering of Post-Disaster Building Damage Levels Using Discrete Wavelet Transform and Principal Component Analysis. Journal of Information Technology and Cyber Security, 3(1), 33-44. https://doi.org/10.30996/jitcs.12270
Issue
Vol 3 No 1 (2025): January
Section
Research Article

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