Clustering Zonasi Daerah Rawan Bencana Alam di Kabupaten Mandailing Natal menggunakan Algoritma K-Means
DOI:
https://doi.org/10.33379/gtech.v7i3.2880Keywords:
Data Mining, clustering, flooding.Abstract
Indonesia often experiences natural disasters, particularly floods in Panyabungan, Mandailing Natal Regency, facing significant challenges in safeguarding the safety and well-being of its population. The high vulnerability to natural disasters and the lack of accurate mapping of flood-prone areas pose obstacles in disaster management. However, through the use of data mining technology and the K-Means algorithm, this research offers a potential solution to effectively identify flood-prone areas. The development of this clustering model and software will assist authorities in reducing losses caused by flood disasters. By mapping vulnerable areas and clustering them into three levels of vulnerability, disaster response can be carried out more quickly and efficiently. This research aims to support disaster mitigation efforts and improve services for the community in Panyabungan. Thus, this study provides an important contribution to enhancing understanding of flood disasters in vulnerable regions in Indonesia and can serve as a reference for making informed decisions to ensure the safety of the population in the future
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Copyright (c) 2023 Ilsa Hidayat, Eva Darnila, Yesy Afrillia
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