<![CDATA[Batik Small and Medium Enterprises (SMEs) play a strategic role in the economy and cultural preservation in Indonesia, yet exhibit varying levels of organizational performance. This heterogeneity demands a more objective and data-driven segmentation approach so that development strategies and policies can be implemented effectively. This study aims to segment Batik SMEs based on organizational performance using the K-Means clustering method. Primary data were obtained through a structured questionnaire survey of 56 Batik SMEs, with organizational performance indicators covering operational and financial performance. All data were normalized using StandardScaler to ensure scale equality between variables. The optimal number of clusters was determined using the Elbow method and the silhouette coefficient. The analysis results showed that a two-cluster configuration was the optimal solution with the highest silhouette coefficient value. The K-Means model resulted in two segments of Batik SMEs with significantly different organizational performance characteristics: SMEs with low organizational performance and SMEs with high organizational performance. Centroid value analysis and cluster visualization confirmed clear cluster separation and a good level of internal homogeneity. These findings indicate that segmenting Batik SMEs based on organizational performance using an unsupervised learning approach is effective in uncovering the structure of performance heterogeneity. This research contributes to providing a data-driven segmentation framework that can be utilized as a tool to support managerial decision-making and policy formulation to sustainably improve the performance and competitiveness of Batik SMEs.]]>
Copyrights © 2026
