Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia

Research Article

k-Means and GIS for Mapping Natural Disaster Prone Areas in Indonesia

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  • @INPROCEEDINGS{10.4108/eai.12-10-2019.2296336,
        author={Suwardi  Annas and Zulkifli  Rais},
        title={k-Means and GIS for Mapping Natural Disaster Prone Areas in Indonesia},
        proceedings={Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia},
        publisher={EAI},
        proceedings_a={MSCEIS},
        year={2020},
        month={7},
        keywords={gis k-means natural disaster rmsd},
        doi={10.4108/eai.12-10-2019.2296336}
    }
    
  • Suwardi Annas
    Zulkifli Rais
    Year: 2020
    k-Means and GIS for Mapping Natural Disaster Prone Areas in Indonesia
    MSCEIS
    EAI
    DOI: 10.4108/eai.12-10-2019.2296336
Suwardi Annas1,*, Zulkifli Rais1
  • 1: Department of Statistics, Universitas Negeri Makassar, Jalan Mallengkeri, Makassar 90224, Indonesia
*Contact email: suwardi_annas@unm.ac.id

Abstract

The number of natural disasters in Indonesia is very high frequency. However, the data collected based on natural disasters has complex structures. One of the efforts to make prevention design is grouping the areas of natural disasters based on their similarities. The proposed methods are k-means to cluster areas and Geographical Information System (GIS) to improve visualization of yielded clusters. This result showed that the best cluster was seven clusters based on root mean square standard deviation (RMSD). Although k-means obtained the best number of clusters, however, it was difficult to present the clusters of natural disaster areas in a map. Therefore, the GIS method can be a useful tool to improve the visualization of k-means.