(Ahmad Dahlan University, Indonesia)(2) Ika Arfiani
(Universitas Ahmad Dahlan, Indonesia)(3) Dwi Normawati
(universitas Ahmad Dahlan, Indonesia)*corresponding author
AbstractMicro, 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 it has a weakness in determining the initial cluster center. This research proposes the K-Means Random Perturbation Bee Colony Optimization (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 optimizing 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 and reach MSMEs beyond Java and internationally, marketing strategies tailored to the characteristics of the region and target market are required. Creative MSMEs can utilize 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.
KeywordsMSMES; Clustering K-Means; RPBCO; Market Expansion
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DOIhttps://doi.org/10.26555/ijain.v12i3.1781 |
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