<![CDATA[Manual sales and inventory management in small culinary enterprises often leads to data inaccuracies, stock mismanagement, and underutilized transactional data. This study aims to design a web-based sales information system integrated with the K-Means clustering algorithm to optimize inventory control at Edam Burger & Frozen Foods. Utilizing the Waterfall methodology, the system was developed using the Laravel framework and MySQL. The analytical engine processed five months of transactional data across fifteen products, applying Min-Max Normalization to equalize the scales of sales volume, revenue, and transaction frequency. The K-Means algorithm successfully segmented the product catalog into three distinct categories based on performance: one high-selling core product (6.7%), three medium-selling secondary items (20%), and eleven low-selling complementary products (73.3%). Black Box Testing confirmed a 100% functional success rate across all system modules. The primary novelty of this research lies in seamlessly embedding the K-Means engine directly into the operational dashboard, overcoming the common barrier of offline, standalone data mining. This integration enables real-time, data-driven procurement strategies, providing actionable recommendations: prioritizing continuous stock availability for high-demand items, scheduling regular restocking for medium items, and minimizing capital tied up in low-moving inventory to reduce food waste. Ultimately, this integrated approach empowers small business owners to transition from intuition-based management to systematic, algorithm-driven inventory optimization. This study successfully bridges the gap between routine transactions and strategic analytics.]]>
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