Designing a Recommendation System at Yense Restaurants

Abstract

The culinary industry in Indonesia is experiencing rapid growth, driven by the increasing use of online food delivery platforms, which have transformed consumer behavior. This study aims to design a web-based recommendation system for Rumah Makan Yense, a restaurant in Jakarta, to enhance customer satisfaction and operational efficiency. The research utilizes the Association Rule Mining method with the FP-Growth algorithm to analyze customer transaction data and identify purchasing patterns. The system provides personalized menu recommendations to customers, addressing the challenge of information overload in menu selection. The findings demonstrate that the recommendation system effectively improves customer satisfaction by delivering relevant menu suggestions, while also increasing sales and service efficiency. The study concludes that implementing such a system enables Rumah Makan Yense to remain competitive in the dynamic culinary industry, showcasing the potential of technology in supporting business operations.