Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province

Authors

  • Nabila Amalia Khairani Department of Information System, School of Industrial and System Engineering, Telkom University
  • Edi Sutoyo Department of Information System, School of Industrial and System Engineering, Telkom University

DOI:

https://doi.org/10.25008/ijadis.v1i1.13

Keywords:

Clustering, Hotspots, K-Means, Unsupervised Learning, Data mining

Abstract

Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries. One of the regions that has the highest fire hotspots is West Kalimantan Province. Forest and land fires have an impact on health, especially on the communities around the scene, as well as on the economic and social aspects. This must be overcome, one of them is by knowing the location of the area of ??fire and can analyze the causes of forest and land fires. With the impact caused by forest and land fires, the purpose of this study is to apply the clustering method using the k-means algorithm to be able to determine the hotspot prone areas in West Kalimantan Province. And evaluate the results of the cluster that has been obtained from the clustering method using the k-means algorithm. Data mining is a suitable method to be able to find out information on hotspot areas. The data mining method used is clustering because this method can process hotspot data into information that can inform areas prone to hotspots. This clustering uses k-means algorithm which is grouping data based on similar characteristics. The hotspots data obtained are grouped into 3 clusters with the results obtained for cluster 0 as many as 284 hotspots including hazardous areas, 215 hotspots including non-prone areas and 129 points that belong to very vulnerable areas. Then the clustering results were evaluated using the Davies-Bouldin Index (DBI) method with a value of 3.112 which indicates that the clustering results of 3 clusters were not optimal.

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Published

2020-04-01

How to Cite

Khairani, N. A. ., & Sutoyo, E. (2020). Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province. International Journal of Advances in Data and Information Systems, 1(1), 9-16. https://doi.org/10.25008/ijadis.v1i1.13
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