Data Mapping System Of Riau Province Fire Potential Using K-Means Clustering Method

  • Rahmaddeni Deni STMIK Amik Riau
  • Andi Kurnianto
Keywords: Data Mining; K-Means Clustering; Hotspots; Visualization of the Mapping.


According to a report from the Riau Province BLHK states that hotspots in Riau Province are always present every year despite the number of hotspots that have been suppressed ( One of the causes is the frequent land clearing occurred as a trigger from a hotspot in Riau Province. There is a need for countermeasures as soon as possible to overcome the problem of hotspots that will cause forest fires. These problems need to be watched out quickly, one of which is to know in advance the hotspots that are likely to emerge based on existing data. Data mining processing is very suitable to be applied in order to produce relevant data to find out the possibility of hotspots. In this study the data grouping was done in the form of a visualization of hotspot mapping using the K-means Clustering method. The parameters used include 3 number of clusters (critical, alert, vigilant), 12 regencies / cities in Riau Province and 3 attributes (hotspots, number of fires, number of events). With the results of the visualization of the mapping using the K-means Clustering method, it is expected to be able to help the relevant parties, namely the Riau Provincial Forest Service in handling early the hotspots that are likely to emerge.


[1] S. Sukamto, I. D. Id, and T. R. Angraini, “Penentuan Daerah Rawan Titik Api di Provinsi Riau Menggunakan Clustering Algoritma K-Means,” JUITA J. Inform., vol. 6, no. 2, p. 137, 2018, doi: 10.30595/juita.v6i2.3172.
[2] R. Goejantoro, “Perbandingan Pengelompokan K-Means dan K-Medoids Pada Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas (Studi Kasus : Data Titik Panas Di Indonesia Pada 28 April 2018) Comparison,” vol. 10, no. April 2018, pp. 143–152, 2019.
[3] N. A. Khairani and E. Sutoyo, “Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province,” Int. J. Adv. Data Inf. Syst., vol. 1, no. 1, pp. 9–16, 2020, doi: 10.25008/ijadis.v1i1.13.
[4] M. Mustofa, “Penerapan Algoritma K-Means Clustering pada Karakter Permainan Multiplayer Online Battle Arena,” J. Inform., vol. 6, no. 2, pp. 246–254, 2019, doi: 10.31311/ji.v6i2.6096.
[5] A. P. Windarto, “Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,” Int. J. Artif. Intell. Res., vol. 1, no. 2, p. 26, 2017, doi: 10.29099/ijair.v1i2.17.
[6] J. Qi, Y. Yu, L. Wang, J. Liu, and Y. Wang, “An effective and efficient hierarchical K-means clustering algorithm,” Int. J. Distrib. Sens. Networks, vol. 13, no. 8, pp. 1–17, 2017, doi: 10.1177/1550147717728627.
[7] S. Naeem and A. Wumaier, “Study and Implementing K-mean Clustering Algorithm on English Text and Techniques to Find the Optimal Value of K,” Int. J. Comput. Appl., vol. 182, no. 31, pp. 7–14, 2018, doi: 10.5120/ijca2018918234.
[8] A. Fauzan, A. Y. Badharudin, F. Wibowo, J. Raya, and D. Purwokerto, “Sistem Klasterisasi Menggunakan Metode K-Means dalam Menentukan Posisi Access Point Berdasarkan Posisi Pengguna Hotspot di Universitas Muhammadiyah Purwokerto ( Clustering System Using K-Means Method in Determining Access Point Position at Muhammadiyah Un,” Juita, vol. III, pp. 25–29, 2014.
[9] M. Robani and A. Widodo, “Algoritma K-Means Clustering Untuk Pengelompokan Ayat Al Quran Pada Terjemahan Bahasa Indonesia,” J. Sist. Inf. Bisnis, vol. 6, no. 2, p. 164, 2016, doi: 10.21456/vol6iss2pp164-176.
[10] J. Han, M. Kamber, and J. Pei, Data Mining, Concepts and Techniques. 2012.