Pengelompokan Tren Kunjungan Wisatawan Mancanegara ke Indonesia Menggunakan Metode Hierarchical Clustering Ward Linkage - Hardianti Hafid; Annisa Syalsabila (Program Studi Statistika FMIPA Universitas Mulawarman, 2026)
This study aims to cluster the trends of international tourist arrivals to Indonesia based on entry points using the Hierarchical Clustering Ward Linkage method. The research employs secondary data on monthly tourist arrivals by entry points from 2008 to 2024, obtained from BPS. Data analysis was conducted through preprocessing, distance measurement using Euclidean distance, clustering with Ward’s method, validation using the Dunn Index, and visualization through dendrograms. The results show that the optimal cluster structure is obtained when the data are grouped into four clusters, with the highest Dunn Index value of 3.201. The clustering reveals distinct patterns: Ngurah Rai International Airport and Soekarno-Hatta International Airport form separate clusters due to their dominant role in international arrivals, while Batam Port constitutes a single cluster driven by cross-border tourism with Singapore. Other entry points are grouped into a homogeneous cluster with relatively smaller volumes of arrivals. These findings highlight the importance of segmented tourism development strategies. Entry points with high tourist volumes require enhanced international capacity and services, while cross-border hubs such as Batam necessitate strengthened regional cooperation. The results provide valuable insights for policymakers in formulating more targeted and effective tourism strategies to enhance Indonesia’s global competitiveness.