TY - GEN
T1 - Comparative Analysis of K-Means and Isodata Algorithms for Clustering of Fire Point Data in Sumatra Region
AU - Sirat, Edo Fadila
AU - Setiawan, Budi Darma
AU - Ramdani, Fatwa
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Forest, land, or residential fire is a familiar phenomenon in Indonesia for last decade. The high number of fire incidents in Indonesia requires attention from the government so that any natural disasters such as forest fires can be resolved. These fire incidents can be analyzed since the data has already been obtained and recorded from satellite. Unfortunately, the data is too large to be analyzed as it was. Based on data obtained from the EOSDIS website, recorded as many as 289,256 fire spots occur in the region of Sumatra in the timeframe between 2001 and 2014. It needs an algorithm to segment the data or clusters the data so that large data can be processed into good information for the user. In this study, a comparative study of clustering algorithms between the K-Means and the Isodata was conducted. Both algorithms used in this study were assessed based on the quality of the clusters produced, which is calculated using Silhouette Coefficient (SC). The final result value of Silhouette Coefficient the K-Means method is 0.999997187, and the Isodata method is 0.999957161. so in this case, K-Means algorithm has a higher SC value compared to the Isodata algorithm in clustering the data of fire spots with a small SC value difference.
AB - Forest, land, or residential fire is a familiar phenomenon in Indonesia for last decade. The high number of fire incidents in Indonesia requires attention from the government so that any natural disasters such as forest fires can be resolved. These fire incidents can be analyzed since the data has already been obtained and recorded from satellite. Unfortunately, the data is too large to be analyzed as it was. Based on data obtained from the EOSDIS website, recorded as many as 289,256 fire spots occur in the region of Sumatra in the timeframe between 2001 and 2014. It needs an algorithm to segment the data or clusters the data so that large data can be processed into good information for the user. In this study, a comparative study of clustering algorithms between the K-Means and the Isodata was conducted. Both algorithms used in this study were assessed based on the quality of the clusters produced, which is calculated using Silhouette Coefficient (SC). The final result value of Silhouette Coefficient the K-Means method is 0.999997187, and the Isodata method is 0.999957161. so in this case, K-Means algorithm has a higher SC value compared to the Isodata algorithm in clustering the data of fire spots with a small SC value difference.
KW - analysis
KW - clustering
KW - fire point
KW - hotspot
KW - Isodata
KW - k-means
UR - https://www.scopus.com/pages/publications/85062184717
U2 - 10.1109/ISYG.2018.8611879
DO - 10.1109/ISYG.2018.8611879
M3 - Conference contribution
AN - SCOPUS:85062184717
T3 - 2018 4th International Symposium on Geoinformatics, ISyG 2018
BT - 2018 4th International Symposium on Geoinformatics, ISyG 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Symposium on Geoinformatics, ISyG 2018
Y2 - 10 November 2018 through 12 November 2018
ER -