Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Scientific Research, Education, and Technology
Zibran, Agil Pranata
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Restaurant Sales Optimization Through Collaborative Filtering and Market Basket Analysis Methods Zibran, Agil Pranata; Sejati, Rr. Hajar Puji
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 4 No. 4 (2025): Vol. 4 No. 4 2025
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v4i4.974

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
Co-Authors