Application of the K-Means Clustering Method in Classifying the Number of Foreign Tourists in West Java

Abstract

West Java Province is a province that has the charm of natural tourism which is quite attractive to local tourists and foreign tourists. In several natural tourist attractions in West Java, the number of tourists has relatively increased. The provincial government has not conducted a mapping analysis of foreign tourists on tourist visits in the West Java area. Foreign tourist data can be used as a reference for the government as a mapping of tourist visits. The research stage uses knowledge discovery in the database. Data processing uses the K-Means method. K-Means is a data main method that provides a group description of an item. The tools used in data processing are the RapidMiner application. The purpose of this study is to classify the number of foreign tourist arrivals at natural tourist spots in West Java. The grouping results obtained are 3 clusters namely, cluster 0 (low) as many as 445 natural tourist attractions, cluster 2 (medium) as many as 880 tourist attractions, and cluster 1 (high) as many as 880 tourist attractions. Natural tourist attractions included in the low cluster can be used as a contribution for the West Java Provincial Government in terms of improving existing facilities at tourist attractions, so that foreign tourists visiting will increase in the future.