Data mining techniques are being used to map the number of foreign guests at Indonesian star hotels

Debi Masri(1*), Sri Kasnelly(2),

(1) Universitas Battuta, Medan, North Sumatra, Indonesia
(2) STAI An-Nadwah Kuala Tungkal, Jambi, Indonesia
(*) Corresponding Author


Statistics on foreign tourist arrivals show an upward trend in recent years. This is beneficial because foreign exchange in the tourism sector is generated by spending by foreign tourists visiting Indonesia. Mapping the number of tourists who visit Indonesia is necessary to determine the extent of a region's natural potential. The number of foreign guests at Indonesia's star hotels is one indicator that can be used. The goal of this study is to use data mining techniques to visualize the mapping pattern of the number of foreign guests at five-star hotels in order to obtain important information. The Central Statistics Agency (abbreviated BPS) provided the data for the number of foreign guests at five-star hotels in 2017-2019. Clustering with the k-medoids technique is the data mining method employed. The Davies-bouldin index (DBI) operator is used to calculate the number of clusters. The number of good clusters was 3 (k=3), with cluster 0 (low) = 20 provinces, cluster 1 (normal) = 23, and cluster 2 (high) = 1 province, and a DBI value of 0.215 (k=3). According to the study's findings, the province of Bali is one of the areas with the greatest potential for foreign tourist visits (high cluster). Meanwhile, there are 20 provinces with a low number of foreign visitor visits, so this can be used to assess how to increase the number of foreign visitor visits to the province.

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