Clustering Tourism Hotspots in Indonesia Using Hotel Guest Data and the K-Means Algorithm

Abstract

Tourism plays a strategic role in increasing foreign exchange earnings and driving economic growth in Indonesia. This study aims to cluster the potential for tourist visits based on the distribution of hotel guests across all provinces in Indonesia using the K-Means Clustering method. The data used in this research is secondary data sourced from the 2021 publication of the Statistics Indonesia, which includes the number of both foreign and domestic guests at star-rated and non-star-rated hotels. The analysis was conducted using SPSS software version 25. The results indicate that all provinces can be grouped into three clusters: those with the highest, moderate, and lowest number of hotel guests. Provinces such as DKI Jakarta, West Java, and Bali are included in the cluster with the highest number of guests, while several provinces in Eastern Indonesia tend to fall into the cluster with fewer hotel guests. These findings are expected to provide insights to support the development of the tourism sector, particularly in the planning of promotional strategies and the enhancement of tourism infrastructure in Indonesia.