Optimization of Clustering Model for Mapping Tourism Potential in Magelang Regency Using K-Means Clustering Algorithm - Kanafi Kanafi; Fatkhurrochman Fatkhurrochman (Electronic Journal of Education, Social Economics and Technology, 2025)
Magelang Regency has various tourist destinations with highly diverse potentials, including nature, history, culture, and religious tourism. However, the management of these tourism potentials still faces serious challenges, particularly in terms of mapping, which has not been well-structured. The numerous different tourist spots with various regional potentials have led to local and international tourists experiencing difficulties in selecting promising destinations. The role of mapping for clustering tourism potentials has become a primary need to accelerate economic growth in Indonesia. If clustering is conducted conventionally, without being based on in-depth data analysis, it can risk producing policies that are not well-targeted. The objective of this study is to map the tourism potentials in Magelang Regency using the optimization of the K-Means Clustering method. This clustering is based on variables: attractiveness, accessibility, facilities, number of visitors, ticket prices, security, and rating values. The research methods include literature review, data collection, data pre-processing, K-Means algorithm implementation and optimization, model evaluation and validation, system visualization development, testing, reporting, and publication. The final result of this study is the clustering of tourism potentials into three 7 clusters, the Silhouette Coefficient test yielded a score of 0.504.