TOURIST CLUSTER ANALYSIS FOR THE DEVELOPMENT OF KE'TE' KESU' TOURISM OBJECT

Abstract

This study aims to analyze the clustering of tourists visiting the Ke'te' Kesu' tourist attraction to support the development of more effective marketing strategies and tourism services. The research employs a K-Means clustering algorithm, which groups tourists based on their socio-economic characteristics and visit preferences. Data were collected through questionnaires distributed to tourists during a specific period. The analysis results indicate that tourists are divided into two main clusters. Cluster one (C1) consists of high-income to very high-income tourists who tend to seek exclusive and premium travel experiences. Cluster two (C2) includes the majority of tourists who are students with low to very low incomes, preferring educational tourism and affordable access. The implications of this study suggest that tourism managers should offer customized travel packages tailored to each cluster, such as premium services for high-end tourists and educational programs for students. Additionally, infrastructure improvements, sanitation facilities, and digital marketing optimization are key recommendations to enhance Ke'te' Kesu's appeal as a cultural tourism destination.