<![CDATA[Information technology has been used for a long time for MSME businesses. Many people who have MSME businesses use online stores to promote their businesses. To be able to attract old customers to shop back to the online store, one of them is by giving a shopping voucher. Shopping vouchers are given to existing customers who have the potential to shop back to online stores. In determining which customer is the right data mining algorithm is needed to find the right information where the customer can shop again. But the error of choosing an algorithm can result in not being optimal in the projected income. In this study, we will analyze and compare the Naive Bayes, J48, and Random Forest Tree algorithms for case studies of online stores. This study involved 7 criteria that would be used to become material in data processing. From the results of this study, a random forest tree is the best algorithm to determine the potential of online store customers. The results of this study are used to help the decision-making process of giving shopping vouchers to customers so that MSME businesses can run and get optimal profits]]>
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