Pengelompokkan Daerah Rawan Bencana di Kabupaten Boyolali Menggunakan Algoritma K-Means
DOI:
https://doi.org/10.36596/jitu.v8i2.1726Keywords:
Disaster, Clustering, K-Means, RapidminerAbstract
The country of Indonesia, which is located in the Southeast Asia region, faces a high challenge of disasters due to demographic, geological and geographical factors that influence the occurrence of disasters, both those caused by natural, non-natural and human factors. In the Province of Central Java, especially Boyolali, disasters pose a significant threat, especially when the weather is uncertain. However, to identify priority areas for the lowest and highest levels of natural disasters in Boyolali, it is necessary to improve using data mining methods, therefore to facilitate analysis and grouping of data in identifying disaster-prone areas in Boyolali, the K-Means clustering algorithm is used. The method used in this research is qualitative and the data is analyzed using the Rapidminer Studio Ver application. 9.10. Based on the results of the analysis, there is a division of disaster-prone areas into 3 clusters in Boyolali Regency, namely very high vulnerability, medium vulnerability, and low vulnerability. The results of calculations with Rapidminer show that there are 3 sub-districts in cluster 2, 2 sub-districts in cluster 1, and 17 sub-districts in cluster 0. It should be noted that manual calculations can produce differences because the cluster center point or centroid is taken randomly for each calculation method. It is hoped that this grouping of areas can be used as a support for the Boyolali regional BPBD to focus on anticipatory steps to reduce the impact of disasters on society that may occur in the future.
References
Eleyan and M. S. Anwar, “Multiresolution Edge Detection Using Particle Swarm Optimization,” Int. J. Eng. Sci. Appl., vol. 1, no. 1, pp. 11–17, 2017.
M. R. Wankhade and N. M. Wagdarikar, “Feature Extraction of Edge Detected Images,” Int. J. Comput. Sci. Mob. Comput., vol. 6, no. 6, pp. 336–345, 2017.
Sunardi, A. Yudhana, and S. Saifullah, “Identity Analysis of Egg Based on Digital and Thermal Imaging: Image Processing and Counting Object Concept,” Int. J. Electr. Comput. Eng., vol. 7, no. 1, pp. 200–208, 2017.
D. Herawati and A. R. Kardian, “Analisis Deteksi Tepi Pada Citra Digital Berbasis JPG Dengan Operator Canny Menggunakan Matrix Laboratory,” J. Ilm. Komputasi, vol. 17, no. 3, pp. 191–208, 2018.
V. Dohare and M. P. Parsai, “A Review of Speed Performance Evaluation of Varios Edge Detection Methods of Images,” Indian J. Comput. Sci. Eng., vol. 8, no. 2, pp. 128–138, 2017.
M. B. Saputra, A. Romadhony, and Adiwijaya, “Analisis Kompresi Lossless JPEG dengan Penambahan komplemen terkompresi deflate,” 2012.
R. Pradeep Kumar Reddy and C. Nagaraju, “Improved Canny Edge Detection Technique Using S-membership Function,” Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 43–49, 2019.
K. R. O. Recio and R. G. Mendoza, “Three-step Approach to Edge Detection of Texts,” Philipp. J. Sci., vol. 148, no. 1, pp. 193–211, 2019.
S. Dhivya and D. R. Shanmugavadivu, “A Big Data Based Edge Detection Method for Image Pattern Recognition - A Survey,” Int. J. Eng. Comput. Sci., vol. 7, no. 03, pp. 23755–23760, 2018.
S. Reno and R. Edyal, “Analisa Perbandingan Deteksi Tepi Citra Foto Menggunakan Algoritma Robert dan Prewitt,” Multinetics, vol. 2, no. 2, p. 11, 2016.
M. R. H. Mohd Adnan, A. Mohd Zain, H. Haron, M. Zulfaezal Che Azemin, and M. Bahari, “Consideration of Canny Edge Detection for Eye Redness Image Processing,” IOP Conf. Ser. Mater. Sci. Eng., vol. 551, no. 1, 2019.
P. Hidayatullah, Pengolahan Citra Digital. Bandung: Informatika Bandung, 2017.
P. Malathi and M. Pushpa, “Image Edge Detection Algorithms Study,” Intern. Res. J. Eng. Technol., vol. 3, no. 6, pp. 786–789, 2016.
H. A. E. El-sennary, M. E. Hussien, and A. E. A. Ali, “Edge Detection of an Image Based on Extended Difference of Gaussian,” Am. J. Comput. Sci. Technol., vol. 2, no. 3, pp. 35–47, 2019.
Niu, “An Improvement Image Subjective Quality Evaluation Model Based on Just Noticeable Difference,” Proceeding Twelfth Int. Conf. Intell. Inf. Hiding Multimed. Signal Process., vol. 2, pp. 101–110, 2017.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 JITU : Journal Informatic Technology And Communication
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
1.png)
