Clustering of Halal MSME Aid Recipients: Uncovering Patterns and Characteristics Using the K-Medoids Method - yelfi Vitriani; Siska Kurnia Gusti (Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi, 2025)
The rapid growth of the halal industry has strengthened the strategic role of Micro, Small, and Medium Enterprises (MSMEs) in meeting market expansion. However, the absence of structured insights regarding the characteristics and patterns of halal MSME aid recipients has hindered the formulation of effective and targeted support programs. This study aims to identify the clustering patterns of halal MSME beneficiaries in Indonesia using the K-Medoids algorithm optimized with Principal Component Analysis (PCA). A total of 129 MSME datasets were collected through validated questionnaires consisting of demographic variables, aid history, business performance, and operational challenges. Preprocessing included data cleaning, transformation, and dimensionality reduction using PCA. The optimal PCA dimension was determined as two components based on the Davies-Bouldin Index (0.1737). K-Medoids clustering produced three optimal clusters validated using Silhouette (0.4602), Davies-Bouldin Index (0.7861), and Elbow Method (K=3). Each cluster shows distinctive characteristics in income range, business legality, type of aid received, challenges, and performance outcomes. The novelty of this research lies in the application of PCA-optimized K-Medoids for halal MSME segmentation, providing insightful foundations for evidence-based policymaking.