Clustering Analysis Using K-Medoids on Poverty Level Problems in Central Java by District/City


Poverty describes the absence of property and poor income or the circumstance that the food, shelter, and clothing needs cannot be met. The performance of Central Java in poverty reduction has risen and declined during the last decade. This research aims to perform mapping analyses utilizing artificial intelligence techniques as clusters on the number of poverty levels in Central Java districts or cities. Since Central Java is after West Java and East Java is the third most populous province, this was necessary to achieve in the last few years through regional mapping of a macro-picture of the poverty level. The dataset used is from the statistics agency website on the number of poor people (millennia) in 2017–2019. The data used are from the Central Java Statistical Agency. The way to map the clusters is using the k-medoids method which is part of data clustering. The number of clusters utilized for mapping poverty levels is high and low. The results showed six provinces (17%) in the high and 29 (83%) in the low. In the high cluster (cluster 1) and in the low cluster (cluster 1) and {18.6, 19.4, 20.1} the final centroid values for each cluster were {293.2, 309.2, 343.5}. The results of mapping can help address the poor in places in which the high cluster (cluster 1), Cilacap District, Banyumas District, Kebumen District, Grobogan District, Pemalang District, and Brebes District are a priority of the government in Central Java province.

Keywords: K-Medoids, clustering, poverty

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