Musdalifa Thamrin, Ida Mulyadi, I Dewa Made Widia, Muhammad Faisal, Suardi Hi Baharuddin, Medy Wisnu Prihatmono, Nurdiansyah, Nasir Usman
Global trade constitutes a highly complex and interdependent system influenced by diverse economic, geographic, and political factors. This study proposes a hybrid clustering framework that integrates density-based spatial clustering of applications with noise (DBSCAN), elbow, and self-organizing maps (SOM) methods to uncover latent structures in international trade patterns. Utilizing averaged trade data from 25 countries spanning the period from 2013 to 2023, the framework identifies distinct clusters based on export-import characteristics. The DBSCAN is employed to detect dense trade hubs and outlier behaviors, the elbow method determines the optimal number of clusters, and SOM facilitates the visualization of non-linear, high-dimensional trade relationships. The analysis reveals three prominent trade clusters: Global Trade Leaders, Emerging Trade Powers, and Niche Exporters, each reflecting varying degrees of trade diversification and dependency. These empirical findings align with established economic theories, including the Heckscher Ohlin model and dependency theory, and provide actionable insights for policymakers seeking to enhance trade competitiveness and regional integration strategies. © 2025, Institute of Advanced Engineering and Science. All rights reserved.
Department of Informatics Management, STMIK Profesional Makassar, Makassar, Indonesia; Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia; Faculty of Vocational Studies, Universitas Brawijaya, Malang, Indonesia; Department of Digital Business, Universitas Dipa Makassar, Makassar, Indonesia
