<![CDATA[In the current Industrial Revolution Era, the growth of Micro, Small, and Medium Enterprises (MSMEs) in the culinary sector in Indonesia has increased very rapidly, many new businesses have emerged and there are old businesses that have gone out of business. The digital revolution opens up huge opportunities in encouraging the growth of Micro, Small, and Medium Enterprises (MSMEs) in the culinary sector in Indonesia, one of which is optimization in various lines of a business. This research was conducted to explore the implementation of machine learning in predicting product demand as a step to increase the efficiency and competitiveness of MSMEs in a very strict digital era. The Support Vector Machine (SVM) and Recurrent Neural Network (RNN) methods are applied in analyzing the key factors that affect the success of digitalization. This case study involves Es Teh Solo Kota Bima Franchise Outlet in Bima City. The results show that digitalization not only increases income but also strengthens the adaptability of MSMEs to market changes. Machine learning has been proven to be able to provide more accurate demand predictions, reduce the risk of overstocking or understocking, and enrich business insights.]]>
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