Random perturbation bee colony optimized k-means approach for optimized MSME data clustering - Lisna lisna Zahrotun; Ika Arfiani; Dwi Normawati (International Journal of Advances in Intelligent Informatics, 2026)
Micro, Small, and Medium Enterprises (MSMEs) play an important role in the economy and need strategic support to grow. The K-Means method is often used for cluster analysis, but has a weakness in determining the initial cluster centre. This research proposes the K-Means Random Perturbation Bee Colony Optimisation (RPBCO) method to overcome the problem. The test results show an increase in cluster accuracy, with the Silhouette Coefficient score increasing by 40.9% (from 0.171 to 0.245). Wilcoxon testing also showed a z-value of -1.342, confirming that RPBCO is superior to standard K-Means. This method proved effective in optimising clusters on a heterogeneous MSMEs dataset. The analysis revealed that creative MSMEs thrive on Instagram, while retail MSMEs perform best on Shopee. To expand the market reach of MSMEs beyond Java and internationally, marketing strategies tailored to the characteristics of the region and target market are required. Creative MSMEs can utilise Instagram Ads to target ASEAN countries, while retail MSMEs can focus on global platforms such as Amazon or Alibaba. Digital literacy training, platform algorithm workshops, and collaborative marketing campaigns can strengthen this strategy. These measures are expected to increase turnover and support the sustainable growth of MSMEs, positively impacting more than one million businesses.