K-Means Algorithm Implementation for Clustering of Foreign Tourists Visiting

Abstract

The tourism sector plays an active role in economic growth for a country. Indonesia, which is one of the ASEAN states, shows that the role of tourism is one of the important sectors in the economy in Indonesia. However, the influence of the tourism sector has not been satisfactory for the government. The role of foreign tourist visits affects the economy in Indonesia by increasing foreign exchange for the country. In 2018, foreign exchange from the tourism sector continued to increase by 15.4 percent on an annual basis. However, it is unfortunate that Indonesia is still relatively small compared to other countries in the number of foreign tourist visits. The purpose of this study is to analyze the application of data mining in classifying the number of foreign tourist visits by Indonesia in ASEAN. The grouping is done by applying the K-Means clustering algorithm method. The data are grouped into 3 clusters, namely the high visit cluster (C1), the medium visit cluster (C2), and the low visit cluster (C3). So that the results obtained from the assessment of foreign tourist visits in ASEAN, namely, C1 namely Malaysia, C2 namely Singapore and Indonesia, and C3 namely the Philippines, Thailand, Vietnam, Myanmar / Burma, Brunei Darussalam, Cambodia, and Laos. The results of this study can be seen that Indonesia is in the medium visit grouping (C2). With this data, it can be a reference for the government to improve the tourism sector in visiting foreign tourists in Indonesia.