Home > Published Issues > 2023 > Volume 14, No. 3, 2023 >
JAIT 2023 Vol.14(3): 454-462
doi: 10.12720/jait.14.3.454-462

Recommendation System of Food Package Using Apriori and FP-Growth Data Mining Methods

Christofer Satria, Anthony Anggrawan *, and Mayadi
University of Bumigora, Mataram, Indonesia; Email: chris@universitasbumigora.ac.id (C.S.), mayadi.yadot@universitasbumigora.ac.id (M.)
*Correspondence: anthony.anggrawan@universitasbumigora.ac.id (A.A.)

Manuscript received September 25, 2022; revised October 4, 2022; accepted October 28, 2022; published May 15, 2023.

Abstract—Currently, the famous restaurant visited by many people is a roadside stall. Generally, the roadside stall sells multiple kinds of food, drink, and snacks. The problem is that roadside stalls have difficulty determining what food items are best-selling to be used as menu packages of choice from almost hundreds of menu items. That is why it needs data mining of roadside stall sales data to explore correlation information and sales transaction patterns for food items that most often become food pairs sold. Therefore, this study aims to analyze the frequency of the most item sets from data sales in food stalls using the Frequent Pattern Growth (FP-Growth) and Apriori data mining methods to recommend which foods/beverages are the best-selling menu packages. The research and development results show that with 980 transaction data with a minimum support value of 20% and a trust value of at least 50% for FP-Growth, it produces eight valid rules. For Apriori, it has five valid rules as a menu package recommendation. The results of the sales trial of the recommended menu package for two months showed that the total sales increased significantly up to 2.37 times greater than the previous sales.
Keywords—data mining, apriori, FP-growth, roadside stall, recommendation system, food package

Cite: Christofer Satria, Anthony Anggrawan, and Mayadi, "Recommendation System of Food Package Using Apriori and FP-Growth Data Mining Methods," Journal of Advances in Information Technology, Vol. 14, No. 3, pp. 454-462, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.