Application of K-Means Algorithm for Cluster Analysis on Poverty of Provinces in Indonesia
Keywords:cluster analysis, k-means, poverty
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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