Integrating Clustering Models and RCA to Identify Emerging Textile Export Destinations for Indonesia - Muhammad Glenn Yunifer; Samidi (Indonesian Journal of Data and Science, 2025)
This research investigates the strategic identification of new export destinations for Indonesian textile products by integrating international market segmentation and product competitiveness analysis. The study employs clustering techniques (K-Means, K-Medoids, and Hierarchical) validated through Silhouette and Davies-Bouldin indices to classify 149 countries based on trade indicators (import growth, trade balance, global market share), economic indicators (population, purchasing power parity, industrial proportion to GDP), and trade barrier indicators (logistics performance index, geographic distance, free trade agreements). Complementarily, the Revealed Comparative Advantage (RCA) framework is applied to evaluate Indonesia’s product-level competitiveness in the global textile market. The results reveal that export opportunities are can be concentrated in 20 countries across Europe, Asia, Africa, the Caribbean, and Melanesia, characterized by positive import growth, significant trade deficits, large market capacities, and relatively low trade barriers. Moreover, Indonesia demonstrates high comparative advantages in artificial and synthetic fibbers, wigs, and leather footwear, while apparel products such as suits, shirts, knitwear, and brassieres represent moderately competitive but globally demanded items. The study concludes that Indonesia’s export strategy should balance high purchasing power markets and emerging economies with high import dependency.