Model of Data mining Clustering Rules on Population Determination of Trade and Accommodation Facilities in Indonesia with K-Means

Abstract

The research aims to conduct a mapping model in the form of the grouping of residents of trade and accommodation facilities according to regions in Indonesia using data mining techniques. This research is a reference specifically for the role of the government in increasing regional income in Indonesia evenly. The data source is obtained from the government statistical data provider website, namely the Central Statistics Agency (BPS) with the URL address www.bps.go.id. The mapping method used is K-Mens and tested with the Rapid Miner software. There are 3 clusters used in mapping the area to the population of trade and accommodation facilities, namely the high (C1), medium (C2), and low (C3) clusters. The results obtained are cluster C1 centroid data, namely ((1527), (810.4), (5865), (6655.3), (323), (315.1)); cluster C2, namely ((286), (199,591), (1327), (2240,227), (93,227), (140,955)); and cluster C3, namely ((139,25), (122,5), (508,833), (919,222), (64,417), (94,444)). The results of the mapping show that in cluster C3, there are 16 provinces with a low population of trade and accommodation facilities.