<![CDATA[Micro, Small, and Medium Enterprises (MSMEs) are strategic pillars of the national economy; however, business owners in Gorontalo City still face challenges such as low digital literacy and limited technology adoption. Current government marketing assistance is considered suboptimal because it still relies on a mass mentoring approach (one-size-fits-all), ignoring the heterogeneous maturity levels of each business unit. This study aims to design and develop a web-based information system that automates MSME clustering using the K-Means algorithm while simultaneously acting as a recommendation engine for personalized digital marketing strategies. The software was developed using the linear sequential Waterfall model. The algorithm's processing successfully mapped a population of 112 data points into three functional clusters: Traditional Micro Enterprises (28 units), Developing Small Enterprises (52 units), and Modern Medium Enterprises (32 units). Functionality testing using the Black Box method showed that the system is 100% valid with no errors. Furthermore, interface usability evaluation using the System Usability Scale (SUS) achieved a score of 84.125, earning an "Excellent" rating. This research contributes by providing an integrated system foundation that shifts the government's coaching paradigm from mass promotion to data-driven precision marketing, thereby accelerating the digital transformation of MSMEs objectively and sustainably.]]>
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