<![CDATA[Micro, Small, and Medium Enterprises (MSMEs) play a vital role in national economic growth, yet their success rate remains low due to limited digital literacy and business planning. This study aims to develop a success prediction model for MSMEs using the K-Nearest Neighbor (KNN) algorithm as a data-driven machine learning approach. A public MSME dataset from Kaggle with 12 financial and non-financial attributes was utilized. Classification was performed with k values of 3, 5, and 7 using the Euclidean distance metric. The best performance was achieved at k = 3 with an accuracy of 91.43%, precision and specificity of 100%. The model effectively identifies business success potential and can support credit evaluation, funding allocation, and MSME development programs. This research demonstrates that integrating machine learning and business data can enhance MSME sustainability and competitiveness in the digital era. In the future, the development of this model is expected to be implemented more widely across the MSME sector to achieve more optimal results.]]>
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