##plugins.themes.academic_pro.article.sidebar##

Published
Sep 11, 2023
Download
PDF
Statistic
Read Counter : 57 Download : 56

Downloads

Download data is not yet available.

Main Article Content

Abstract

Forest and land fire significantly impact the balance of the environment, such as haze pollution, destruction of ecosystems, the high release of carbon in the air, deterioration of health, and losses in various other fields. Based on these factors, developing an early warning system is essential to prevent forest fires, especially in forest and land areas. One of the data that can be used to monitor areas where there are frequent fires is hotspot data taken from the NASA MODIS Fire satellite. Data mining techniques are carried out to process the hotspot data so that the distribution of hotspot swarms is obtained. The data on the distribution of the clustering of hotspots are used to detect areas that are prone to fire from year to year. This study used the K-Means clustering algorithm. The data used in this study is hotspot data from Malang District, Indonesia. The range of hotspot data from January 2018 to June 2022. We use Silhouette coefficient testing to get the best number of classes in the cluster—this study's most recent application of the K-means clustering method to analyze hotspot distribution in a spatial-temporally. We use hotspot data in Malang's forest and land area using hotspot confidence levels >80%.

Article Details

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Kirana, A. P., Astiningrum, M., Vista, C. B., Bhawiyuga, A., & Amrozi, A. N. (2023). Spatio-Temporal Pattern Analysis of Forest Fire in Malang based on Remote Sensing using K-Means Clustering. International Journal of Multidisciplinary: Applied Business and Education Research, 4(8), 3046-3057. https://doi.org/10.11594/ijmaber.04.08.37

References

A. P. Kirana, I. S. Sitanggang, and L. Syaufina, “Hotspot Pattern Distribution in Peat Land Area in Sumatera Based on Spatio Temporal Clustering,” Procedia Environ. Sci., vol. 33, pp. 635–645, 2016, doi: 10.1016/j.proenv.2016.03.118.
A. P. Kirana, I. S. Sitanggang, and L. Syaufina, “Poisson Clustering Process on Hotspot in Peatland Area using Kulldorff’s Scan Statistics Method,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 13, no. 4, p. 1376, Dec. 2015, doi: 10.12928/telkomnika.v13i4.2272.
A. Kirana, I. Sitanggang, L. Syaufina, and A. Bhawiyuga, "Spatial and Temporal Clustering Analysis of Hotspot Pattern Distribution of Critical Land in Kalimantan, Indonesia," IOP Conf. Ser. Earth Environ. Sci., vol. 528, no. 1, p. 012042, Jul. 2020, doi: 10.1088/1755-1315/528/1/012042.
A. P. Kirana, R. Ariyanto, A. R. T. H. Ririd, and E. L. Amalia, "Agricultural drought monitoring based on vegetation health index in East Java Indonesia using MODIS Satellite Data," IOP Conf. Ser. Mater. Sci. Eng., vol. 732, no. 1, p. 012063, Jan. 2020, doi: 10.1088/1757-899X/732/1/012063.
A. Kirana, A. Ririd, R. Ariyanto, E. Amalia, and A. Bhawiyuga, "Spatiotemporal Pattern Distribution of Drought Area using MODIS Vegetation Health Index. Case Study: Paddy Field in East Java, Indonesia," IOP Conf. Ser. Earth Environ. Sci., vol. 528, no. 1, p. 012056, Jul. 2020, doi: 10.1088/1755-1315/528/1/012056.
Farid Assifa, “Kebakaran Gunung Semeru Capai 93,3 Hektare,” kompas, 2019. https://regional.kompas.com/read/2019/09/29/13364011/kebakaran-gunung-semeru-capai-933-hektare.
H. Sun, X. Zhao, Y. Chen, A. Gong, and J. Yang, "A new agricultural drought monitoring index combining MODIS NDWI and day–night land surface temperatures: a case study in China," Int. J. Remote Sens., vol. 34, no. 24, pp. 8986–9001, Dec. 2013, doi: 10.1080/01431161.2013.860659.
I. S. Sitanggang, A. A. N. Risal, and L. Syaufina, “Incremental Clustering on Hotspot Data as Forest and Land Fires Indicator in Sumatra,” IOP Conf. Ser. Earth Environ. Sci., vol. 187, p. 012043, Nov. 2018, doi: 10.1088/1755-1315/187/1/012043.
J. MacQueen, "Some methods for classification and analysis of multivariate observations," Univ. Calif. Press, vol. 1, pp. 281–297, 1967, [Online]. Available: https://projecteuclid.org/ebooks/berkeley-symposium-on-mathematical-statistics-and-probability/Proceedings-of-the-Fifth-Berkeley-Symposium-on-Mathematical-Statistics-and/chapter/Some-methods-for-classification-and-analysis-of-multivariate-observations/bsmsp/1200512992.
malangkab.go.id, “Kabupaten Malang Satu Data,” 2018. [Online]. Available: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjFlPe-zcL5AhU71XMBHXULBToQFnoECAgQAQ&url=http%3A%2F%2Fmalangkab.go.id%2Fuploads%2Fdokumen%2Fmalangkab-Kondisi%2520Geografis.pdf&usg=AOvVaw11Hr5mNPHeNDwUU8IvScCg.
M. A. Bianto, S. Rahayu, and M. Huda, “Perancangan Sistem Pendeteksi Plagiarisme Terhadap Topik Penelitian Menggunakan Metode K-Means Clustering Dan Model Bayesian,” Semin. Nas. Teknol. Inf. dan Multimed., pp. 19–24, 2018.
Ministry of Environment and Forestry of the Republic of Indonesia, "Forest Fire," 2022. https://dataalam.menlhk.go.id/karhutla/terbaru.
P. J. Rousseeuw, "Silhouettes: A graphical aid to the interpretation and validation of cluster analysis," J. Comput. Appl. Math., vol. 20, pp. 53–65, Nov. 1987, doi: 10.1016/0377-0427(87)90125-7.