A Hybrid Approach Using K-Means Clustering and the SAW Method for Evaluating and Determining the Priority of SMEs in Palembang City - Terttiaavini, Terttiaavini (Intelligent System and Computation, 2024)
The current efforts to develop Small and Medium Enterprises (SMEs) are still facing challenges in setting appropriate targets. Although the Palembang City Cooperative and SME Agency has launched various programs and initiatives to support SME development, they have not yet successfully identified the SMEs that should be given priority for development. This study aims to apply a hybrid approach that combines the K-Means Clustering method and Simple Additive Weighting (SAW) to evaluate and prioritize SME development in Palembang City. The K-Means Clustering method is used to group SMEs based on their characteristics, while SAW provides preference values ( ). The SME data was obtained from the Palembang City Cooperative and SME Agency, covering 362 SME units. The K-Means Clustering results yielded two clusters: Cluster 0 as the High Growth Cluster and Cluster 1 as the Stability and Improvement Cluster. Validation using cross-validation showed that this model achieved an accuracy of 99.72%. The SAW analysis on Cluster 0 indicated that the Kopi Kaljo SME received the highest priority with a preference value of 45.71. This study confirms that this hybrid approach is effective in grouping SMEs based on their characteristics and prioritizing them based on data-driven evaluation. The research results are expected to help the Palembang City Cooperative and SME Agency design more effective and targeted assistance programs to optimize the contribution of SMEs to local economic growth to the maximum extent.