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Journal : Building of Informatics, Technology and Science

Analisa Perbandingan Algoritma K-Means dan DBSCAN Untuk Klastering Wilayah Rawan Bencana Lay, Nathanael Kenneth; Arisandi, Desi; Christanto, Henoch Juli
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8727

Abstract

West Java Province exhibits high disaster vulnerability, necessitating accurate risk zone mapping for mitigation purposes. This study aims to conduct a comparative performance analysis of K-Means and DBSCAN algorithms for clustering disaster-prone areas. The comparison is important because K-Means, as a centroid-based algorithm, is sensitive to outliers, whereas DBSCAN, as a density-based method, is theoretically more suitable for complex disaster-risk data and capable of identifying anomalies. A quantitative approach was applied to secondary data on disaster incidents (Floods, Landslides, Earthquakes, Fires, and Extreme Weather) for the 2015-2024 period across 27 regencies/cities. Following normalization using StandardScaler, model performance was evaluated through Silhouette Score (SI) comparison and visual analysis of the formed cluster structures. The results reveal a paradoxical finding: although K-Means (K=2) numerically outperformed DBSCAN (0.468) with an average SI of 0.502, it demonstrated internal validity failure due to negative silhouette scores indicating misclassification in extreme regions. Conversely, DBSCAN proved superior in representing the natural data structure with its capability to isolate 6 anomalous regions as noise. Further temporal sensitivity analysis revealed significant risk dynamics, where the number of noise regions increased drastically from 5 regions in the 2015-2019 period to 10 regions in the 2020-2024 period. This indicates that disaster patterns in West Java are becoming increasingly irregular and unpredictable, positioning DBSCAN as the recommended robust method for complex risk mapping.
Co-Authors Andri Sutrisno Andrian, Gion Anjelie, Mega Karina Anthony Gunawan, Anthony Arumsari, Chysanti Arya Dwi Saputra Bagus Mulyawan Bellarmino, Edward Beng , Jap Tji Bernard Dean Sofli Budianto, Gabriel Arnold Budiman, Dennis Zefanya Bunardi, Benny Christanto, Henoch Juli Dali Santun Naga Denny Andreas, Denny Dharmawan, William Susanto Dinatha, Vienchenzia Oeyta Dwitama Edang, Ignasius Alfon Hanjuk Edi Chandra, Edi Effendi, Absarani Maharani Ery Dewayani Ery Dewayani Feni Suwanto, Feni Frans Lienardi, Frans Fusta, Derren Gulo, Trinatalis Gumarus, Nathanael Hardjo, Ayra Diputera Jap Tji Beng Jeffri Alfred, Jeffri Johyandi Lukmana Juwita Juwita, Juwita Kenny Kenny Lay, Nathanael Kenneth Lely Hiryanto Limbor, Ellen Gabriel Mahardika Darmawan Kusuma Wardana Manik , Laura Bernadeth Manik, Laura Bernadeth Mat Syuroh Meilita Chintya Michael Andrian Mudasir Mudasir Mursyida, Rika Muslimin, Murni Nagadiraja, Bramata Nagm, Fouad Nani Widya, Nani Noeratri Andanwerti Nurkholiza, Rahmiyana Nuuridha Matiin Pakata, Rosmiati Pakata, Salsabila Pranata, Edward Brainard Qadriah, Sekar Aurannisa Ramdhani Rajasa, Daud Angga Ramadhani, Kalya Sukma Richard Richard Ronald Chandra Salsabila, Tasya Mulia sandrya, vincent Sefira, Fasia Meta Sri Tiatri Sufisan Sufisan Teny Handhayani Tjengharwidjaja, Adryan Tony Tony Tony TRI SUTRISNO Vebrianto, Daniel Vincentius Gunawan, Vincentius Viny Christanti M WAHYUNI Wasino Wasino Wasino Wasino Wijaya, Angeline Carolina Winata, Arya Hartono Wulandari, Lita Eka ZAHRO, TIARA