Abstract
Industrial catering services that supply daily meals to factory workers face recurring operational challenges, particularly in planning menus that are balanced in terms of cost, ingredient availability, and nutritional variety. Manual menu planning is time-consuming and often results in inconsistent decisions, especially when ingredient stocks fluctuate daily. This study presents the design and implementation of an automatic daily menu recommendation system for Puteri Catering, an industrial catering service in Sidoarjo, East Java, Indonesia. The system integrates three complementary approaches: Constraint-Based Filtering to validate menu candidates against current ingredient stock and budget limits, K-Means Clustering to categorize menus into three cost groups (Cheap, Moderate, and Expensive) based on their production cost per serving (HPP), and Apriori Association Rule Mining to discover frequent menu combination patterns from historical transaction data. The system was developed using the Laravel 11 framework with a MySQL database, while the machine learning components were implemented in Python using the Scikit-learn and MLxtend libraries. The dataset used consists of 43 menu items, 62 raw ingredients, and 60 historical daily transactions. Evaluation results show that K-Means clustering achieved a Silhouette Score of 0.5794, indicating a good separation between clusters. The Apriori algorithm successfully generated 83 association rules, with the strongest rule being Rawon → Mendol with a confidence of 88.9% and a lift of 3.56. Functional testing using the Black Box method across 47 test cases covering 9 system modules yielded a 100% success rate. The system successfully generates three menu package recommendations per day, each with automatic ingredient stock deduction, providing a practical and data-driven solution for daily operational planning in industrial catering services.