Restaurant Sales Optimization Through Collaborative Filtering and Market Basket Analysis Methods

Agil Pranata Zibran; Rr. Hajar Puji Sejati

doi:10.58526/jsret.v4i4.974

Authors

Agil Pranata Zibran Universitas Teknologi Yogyakarta,Indonesia
Rr. Hajar Puji Sejati Universitas Teknologi Yogyakarta, Indonesia

DOI:

https://doi.org/10.58526/jsret.v4i4.974

Keywords:

Collaborative Filtering, , Market Basket Analysis, Recommendation System, AADIE

Abstract

The wide variety of menu options in restaurants often triggers the paradox of choice phenomenon, a condition in which customers struggle to make decisions and tend to order the same items repeatedly. This behavior hinders the exploration of new products and limits the effectiveness of promoting featured menus, ultimately leading to revenue stagnation for the restaurant. This study aims to design and develop an Android-based restaurant ordering system to optimize sales through the real-time delivery of personalized menu recommendations. The system was developed using the ADDIE model (Analyze, Design, Development, Implementation, Evaluation) with technical implementation based on the Flutter and Golang frameworks. This research integrates Item-Based Collaborative Filtering and Market Basket Analysis (MBA). The MBA algorithm is utilized to efficiently calculate support values and item-to-item correlations as a statistical foundation for Collaborative Filtering in generating accurate predictions. Black Box testing validates that all functional features, including menu management, transactions, digital reservations, and the recommendation module, operate properly. In conclusion, the integration of these two methods has proven effective in improving the relevance of menu recommendations, supporting cross-selling and up-selling strategies, and significantly enhancing operational efficiency and customer decision-making quality

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