<![CDATA[Kedai Mitetaki still implements manual sales and transaction recording processes through direct orders and social media, such as WhatsApp, Instagram, and TikTok. This condition causes transaction data to be not optimally utilized to analyze customer purchasing patterns and may lead to recording errors and delays in managing sales reports. This study aims to develop a web-based e-commerce information system that supports the management of menus, customers, orders, transactions, and sales reports, as well as applies the Apriori algorithm to generate menu recommendations based on customer purchasing patterns. The research method used is qualitative with a descriptive approach. The system development uses the Waterfall method, which consists of analysis, design, coding, testing, and maintenance stages. The analysis technique uses the Apriori algorithm with minimum support and minimum confidence parameters to identify relationships between menu items that are frequently purchased together. System testing was conducted using Black Box Testing on the main system functions and the Apriori analysis process. The results show that the web-based e-commerce information system was successfully developed and can support menu management, ordering, transactions, payments, and sales reports in a more structured manner. The testing results show that all tested scenarios obtained valid status. The implementation of the Apriori algorithm also successfully generated association rules and menu recommendations based on customer transaction data, helping customers select menus and assisting owners in managing sales information.]]>
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