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


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