Mapping the Contribution of Kasepuhan Ciptagelar Tourism to Regional Income and Community Economy using the K-Means Clustering Method - Gustian, Dudih; Sembiring, Falentino; Rahma, Fanny; Suwanto, Aat; Ramli, Azizul Azhar; Supangat (Universitas Negeri Semarang, 2026)
Purpose: This study aims to map the contribution of Kasepuhan Ciptagelar tourism to regional income and the community economy as part of the UNESCO Global Geopark Ciletuh–Pelabuhanratu, Sukabumi. The research addresses the problem of limited infrastructure, poor accessibility, and low optimization of local tourism potential that hinders its contribution to regional income. The objective is to provide a data-driven understanding of tourism’s economic and socio-cultural impact. This study aims to map the contribution of Kasepuhan Ciptagelar tourism to regional income and the community economy as part of the UNESCO Global Geopark Ciletuh–Pelabuhanratu, Sukabumi. The research addresses the problem of limited infrastructure, poor accessibility, and low optimization of local tourism potential that hinders its contribution to regional income. The objective is to provide a data-driven understanding of tourism’s economic and socio-cultural impact. Methods: A mixed-method approach was used, integrating quantitative and qualitative analyses. Data were collected through questionnaires and interviews from three main respondent groups: local community (58%), tourists (31%), and government/MSMEs (11%). The K-Means Clustering algorithm was applied to classify perceptions into three contribution levels-high, medium, and low-while an ANOVA test was used to examine statistical differences among clusters. Results: The clustering results revealed three contribution categories: C1 (low) with 186 data points, C2 (medium) with 195 data points, and C3 (high) with 216 data points. The high cluster demonstrated a strong positive contribution to regional income, local economy, and infrastructure development, although challenges remain in socio-cultural sustainability. The ANOVA test confirmed significant differences in economic and infrastructure variables, while destination attractiveness was relatively uniform across clusters. Novelty: This study provides a multidimensional mapping model that integrates socio-economic and participatory data with K-Means clustering to analyze cultural tourism contribution. It introduces a data visualization information system prototype for policymakers to evaluate tourism performance interactively. The research offers new insights into evidence-based strategies for sustainable and community-centered cultural tourism development.