Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province
Open Access
- 1 April 2020
- journal article
- Published by Indonesian Scientific Journal in International Journal of Advances in Data and Information Systems
- Vol. 1 (1), 9-16
- https://doi.org/10.25008/ijadis.v1i1.13
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.Keywords
This publication has 15 references indexed in Scilit:
- Fuzzy Soft Set for Rock Igneous ClasificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Application of adaptive neuro-fuzzy inference system and chicken swarm optimization for classifying river water qualityPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- A Soft Set-based Co-occurrence for Clustering Web User TransactionsTELKOMNIKA (Telecommunication Computing Electronics and Control), 2017
- Predicting Student Performance by Using Data Mining Methods for ClassificationCybernetics and Information Technologies, 2013
- Advances in K-means ClusteringPublished by Springer Science and Business Media LLC ,2012
- An efficient algorithm for incremental mining of temporal association rulesData & Knowledge Engineering, 2010
- Identifying hotspots for plant invasions and forecasting focal points of further spreadJournal of Applied Ecology, 2009
- Top 10 algorithms in data miningKnowledge and Information Systems, 2007
- Forest Fire ManagementPublished by Elsevier BV ,2001
- Some new indexes of cluster validityIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1998