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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Fountain of Informatics Journal IT JOURNAL RESEARCH AND DEVELOPMENT Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Perangkat Lunak ILKOMNIKA: Journal of Computer Science and Applied Informatics Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen JENTIK Journal of Information Technology and Cyber Security JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Syahiduz Zaman
Teknik Informatika Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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Journal : Journal of Information Technology and Cyber Security

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Clustering of Post-Disaster Building Damage Levels Using Discrete Wavelet Transform and Principal Component Analysis Purnamasari, Putri; Imamudin, Mochamad; Zaman, Syahiduz; Syauqi, A’la; Almais, Agung Teguh Wibowo
Journal of Information Technology and Cyber Security Vol. 3 No. 1 (2025): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.12270

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.
Co-Authors AA Sudharmawan, AA Abdush Shomad Bukhori Agung Teguh Wibowo Almais Ainul Yaqin Alya Fitria Baskoro, Sentot Eko Caterina, Yessy Dewa Bagus Alif Rosyadi Erfin, Mahadir Muhamad Fitria, Alya Ida Ayu Putu Sri Widnyani M. Ainul Yaqin Mahadir Muhamad Erfin Maharani, Hamidah Lutfiyanti Maulidah Fithriyah Mochamad Imamudin Mufiddin, Rifqi Munajatul Azizah Putri Purnamasari Rafika Syahranita Rifqi Mufiddin Siti Muslihaeny Suhartono Syafiqah, Annisa Syauqi, A’la Tamaulina Br Sembiring Taufik Ardiansyah Putra Totok Chamidy Yessy Caterina