A Proposed Menu Engineering-Based Business Intelligence Design Using K-Means Algorithm

Abstract

Business continuity is greatly influenced by the menu, especially in the culinary industry. Micro, Small, and Medium Enterprises (MSMEs) account for 64.1 million units or around 99% of all businesses in Indonesia. For many years, MSME has been using menu analyses to keep its menu optimized. However, this is not enough since 50% of MSMEs are failing in their first 5 years due to poor decision-making as a result of a lack of knowledge. Based on that problem, there is a necessity for menu analysis and tools to assist in decision-making, also called Business Intelligence. The method used consists of three stages: data collection, business intelligence design, as well as analysis and results. The BI design focuses on menu engineering using the K-Means algorithm to divide menu items into four unique clusters according to Kasavana-Smith's menu engineering concept. After validating its findings with the Davies-Boudlin Index eval_uation, it concludes that a four-cluster solution is most optimal among other value-cluster. This study aims to assist business owners in making better decisions, and it may be used as a reference for business owners by providing suggestions based on the menu review analysis.