Menu Recommendation System for a Coffee Shop Using Association Rule Mining Algorithm

Abstract

Coffee shops have evolved beyond being mere places to enjoy coffee; they have become social hubs and spaces for work or relaxation in the modern era. To address the growing competition and constant market changes, coffee shops must develop strategies to enhance customer experiences and operational efficiency. One such strategy is implementing a web-based sales and ordering information system. This study examines the relevance and necessity of adopting information systems in the coffee industry, focusing on small to medium-sized coffee shops, thus addressing a gap in the existing literature. The Waterfall methodology was employed in system development, encompassing requirement analysis, design, implementation, testing, and maintenance phases. The requirement analysis phase identifies the system's functional and non-functional needs, while the design phase includes the creation of Use Case, Activity, Entity Relationship (ER) diagrams, and user interfaces. The system was implemented using HTML, CSS, and JavaScript for the front-end, with PHP and the Laravel Framework for the back-end. System testing was conducted using Black Box Testing and the System Usability Scale (SUS) to ensure optimal performance. The system achieved a SUS score of 75.7, indicating good usability and user acceptance.