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

Nabila Amalia  Khairani; Edi Sutoyo

doi:10.25008/ijadis.v1i1.13

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.

Downloads

Download data is not yet available.

Plum Analytics		Dimensions

References

KLHK, Status Hutan dan Kehutanan Indonesia 2018. 2018.

K. dan G. Badan Meteorologi, “Satelit Hotspot MODIS [Indonesia Barat] BMKG,” 2019. .

I. Ibáñez, J. A. Silander, J. M. Allen, S. A. Treanor, and A. Wilson, “Identifying hotspots for plant invasions and forecasting focal points of further spread,” J. Appl. Ecol., vol. 46, no. 6, pp. 1219–1228, Nov. 2009.

J. Han, M. Kamber, and J. Pei, Data Mining, Concepts and Techniques. 2012.

J. C. Bezdek and N. R. Pal, “Some new indexes of cluster validity,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 28, no. 3, pp. 301–315, 1998.

S. H. Endrawati, “Analisis Data Titik Panas (Hotspot) dan Areal Kebakaran Hutan dan Lahan tahun 2016,” in Kementerian Lingkungan Hidup dan Kehutanan, 2016, p. 1.

“Forest Fire Management,” pp. 527–583, Jan. 2001.

T. Amit Garg, “Modelling Fire Hazard in Pine Zone of Uttarakhand,” 2017.

L. Giglio, “MODIS Collection 4 Active Fire Product User ’ s Guide Version 2 . 3,” Sites J. 20Th Century Contemp. French Stud., vol. Version 2., no. February, p. 44, 2007.

E. Sutoyo, R. R. Saedudin, I. T. R. Yanto, and A. Apriani, “Application of adaptive neuro-fuzzy inference system and chicken swarm optimization for classifying river water quality,” in Proceeding - 2017 5th International Conference on Electrical, Electronics and Information Engineering: Smart Innovations for Bridging Future Technologies, ICEEIE 2017, 2018, vol. 2018-Janua, pp. 118–122.

E. Sutoyo and A. Almaarif, “Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 1, pp. 95–101, Feb. 2020.

I. T. R. Yanto, E. Sutoyo, A. Apriani, and O. Verdiansyah, “Fuzzy Soft Set for Rock Igneous Clasification,” in 2018 International Symposium on Advanced Intelligent Informatics (SAIN), 2018, pp. 199–203.

A. P. Slavia, E. Sutoyo, and D. Witarsyah, “Hotspots Forecasting Using Autoregressive Integrated Moving Average ( ARIMA ) for Detecting Forest Fires,” in 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), 2019, pp. 92–97.

D. Kabakchieva, “Predicting student performance by using data mining methods for classification,” Cybern. Inf. Technol., vol. 13, no. 1, pp. 61–72, 2013.

A. Aninditya, M. A. Hasibuan, and E. Sutoyo, “Text Mining Approach Using TF-IDF and Naive Bayes for Classification of Exam Questions Based on Cognitive Level of Bloom’s Taxonomy,” in 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), 2019, pp. 112–117.

E. Sutoyo and A. Almaarif, “Twitter Sentiment Analysis of The Relocation of Indonesia’s Capital City,” Bull. Electr. Eng. Informatics, vol. 9, no. 04, 2019.

E. Sutoyo, I. T. R. Yanto, Y. Saadi, H. Chiroma, S. Hamid, and T. Herawan, “A Framework for Clustering of Web Users Transaction Based on Soft Set Theory,” in Springer, 2019, pp. 307–314.

E. Sutoyo, I. T. R. Yanto, R. R. Saedudin, and T. Herawan, “A soft set-based co-occurrence for clustering web user transactions,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 15, no. 3, 2017.

T. F. Gharib, H. Nassar, M. Taha, and A. Abraham, “An efficient algorithm for incremental mining of temporal association rules,” Data Knowl. Eng., vol. 69, no. 8, pp. 800–815, Aug. 2010.

X. Wu et al., “Top 10 algorithms in data mining,” Knowl. Inf. Syst., vol. 14, no. 1, pp. 1–37, 2008.

J. Wu, Advances in K-means clustering: a data mining thinking. Springer Science & Business Media, 2012.