Application of K-Means Algorithm for Cluster Analysis on Poverty of Provinces in Indonesia
DOI:
https://doi.org/10.21512/comtech.v7i2.2254Keywords:
cluster analysis, k-means, povertyAbstract
The objective of this study was to apply cluster analysis or also known as clustering on poverty data of provinces all over Indonesia.The problem was that the decision makers such as central government, local government and non-government organizations, which involved in poverty problems, needed a tool to support decision-making process related to social welfare problems. The method used in the cluster analysis was kmeans algorithm. The data used in this study were drawn from Badan Pusat Statistik (BPS) or Central Bureau of Statistics on 2014.Cluster analysis in this study took characteristics of data such as absolute poverty of each province, relative number or percentage of poverty of each province, and the level of depth index poverty of each province in Indonesia. Results of cluster analysis in this study are presented in the form of grouping of
clusters' members visually. Cluster analysis in the study can be used to identify more quickly and efficiently on poverty chart of all provinces all over Indonesia. The results of such identification can be used by policy makers who have interests of eradicating the problems associated with poverty and welfare distribution in Indonesia, ranging from government organizations, non-governmental organizations, and also private organizations.
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References
BPS. (2015). Gini Ratio Menurut Provinsi Tahun 1996, 1999, 2002, 2005, 2007-2013. Retrieved August 3, 2015 from http://www.bps.go.id/linkTabelStatis/view/id/1493
BPS. (2015). Konsep Penduduk Miskin. Retrieved August 3, 2015 from http://www.bps.go.id/Subjek/view/id/23#subjekViewTab1|accordion-daftar-subjek1
Gothai, E., Balasubramanie, P. 2012. An efficient way for clustering using alternative decision tree. American Journal of Applied Science, 9, 531-534.
Han, J., Kamber, M. (2012). Data Mining: Concepts and Techniques (4th ed.). San Francisco: Morgan Kaufmann Publishers.
Hossain, J., Sani, N. F. M., Mustapha A., & Affendey L. S., 2013. Using feature selection as accuracy benchmarking in clinical data mining. Journal of Computer Science, 9, 883-888.
Kumar, S. P., Ramaswami, K. S. (2011). Fuzzy modeled k-cluster quality mining of hidden knowledge for decision support. Journal of Computer Science, 7, 1652-1658.
Multifiah. (2011). Telaah Kritis Kebijakan Penanggulan Kemiskinan Dalam Tinjauan Konstitusi. Journal of Indonesian Applied Economics, 5(1), 1-27. Retrieved August 4, 2015 from http://jiae.ub.ac.id/index.php/jiae/article/view/109
Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance. International Journal of Computer Science and Information Security (IJCSIS), 7(1). Retrieved on August 3rd, 2015
from http://arxiv.org/ftp/arxiv/papers/1002/1002.2425.pdf
Purwanto, E. A. (2007). Mengkaji Potensi Usaha Kecil dan Menengah (UKM) untuk Pembuatan Kebijakan Anti Kemiskinan di Indonesia. Jurnal Ilmu Sosial dan Ilmu Politik, 10(3), 295-324.
Silwattananusarn, T., & Tuamsuk, K. (2012). Data Mining and Its Applications for Knowledge Management: A Literature Review from 2007 to 2012. International Journal of Data Mining & Knowledge Management Process (IJDKP), 2(5). Retrieved on August 3, 2015 from http://arxiv.org/ftp/arxiv/papers/1210/1210.2872.pdf
Tajunisha, S. (2010). Performance analysis of k-means with different initialization methods for high dimensional data. International Journal of Artificial Intelligence & Applications (IJAIA), 1(4), 44-52. Retrieved on August 3, 2015 from https://www.academia.edu/12640770/Performance_analysis_of_kmeans_with_different_initialization_methods_for_high_dimensional_data
Tayal, M. A., & Raghuwanshi, M. M. (2011). Review on Various Clustering Methods for the Image Data. Journal of Emerging Trends in Computing and Information Sciences, 2, 34-38, Special
Issue.
Wang, H., & Song, M. (Desember, 2011). Ckmeans.1d.dp: Optimal k-means clustering in One Dimension by Dynamic Programming. The R Journal, 3(2), 29-32.
Xu, R., Wunsch, D. C. (2005). Survey of Clustering Algorithms. IEEE Transactions on Neural Networks, 16 (3), 645 - 678.
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