Hierarchical Clustering-Based Geospatial Analysis for a Personalized Tourism Destination Recommender System

This study applies hierarchical clustering with cosine distance to analyze and visualize tourist travel patterns across various provinces in Indonesia. The methodology includes grouping tourism travel data using hierarchical clustering, assessing cluster quality with the silhouette score, and visualizing the results through dendrograms and geospatial maps. The clustering results reveal distinct travel patterns across regions, which can form more targeted tourism recommendations. The evaluation shows that hierarchical clustering achieved the highest silhouette score of 0.843, demonstrating superior performance compared to the other methods. These findings contribute to the field of tourism management and decision-making by offering data-driven insights for more personalized and effective travel planning.