A Proposed Menu Engineering-Based Business Intelligence Design Using K-Means Algorithm - Jonathan Shinray Fang; Dwi Hosanna Bangkalang (Publikasi Indonesia, 2024)
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.