Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

Abstract

Existing transaction data is only recorded and stored as a sales transaction memorandum, so it has not been utilized optimally. The data is only stored and used as transaction history. The availability of a lot of data and having a pattern of sales transactions that are similar to MSME Cafe Over Limit will be utilized by using data mining science. This research uses the association rules method. Implementation of fp-growth to get item combinations. The purpose of this research is to make it easier for MSMEs to determine menu recommendations for customers. The fp-growth algorithm is used to process as many as 2038 transaction data with a minimum support value of 10%, while for a minimum confidence value of 50%. So that there are 3 rules, namely "if you order Mariam chocolate cheese milk then the customer will order Kopsus Overlimit", from this rule it will form a support value of 10.79%, using a confidence value of 54.19% and a lift ratio of 0.93. Furthermore "if you order Kopsus Overlimit then you will order tofu at grandma's house", from the rule it will produce a support value of 34.69%, with a specified confidence value of 59.76%, so the lift ratio value is 1.15. The last rule "if you order tofu at grandma's house, the customer orders Kopsus Overlimit", from the rule that occurs, the support value is 34.69%, with a confidence value of 66.7% and a lift ratio of 1.15. The results of the study found the two best rules, namely "if the customer orders over-limit Kopsus, he will order tofu at grandma's house" and "if he orders tofu at grandma's house, the customer orders over-limit Kopsus". Based on the results of the rules formed, it can be concluded that only two rules can be categorized as valid and can be used as a reference in food and beverage menu recommendations at MSME Cafe Over Limit. So the results of this study can be useful to be applied to MSMEs, especially in terms of menu recommendations.