Application of the FP-Growth Method to Determine Drug Sales Patterns
Keywords:Data Mining; FP-Growth; Frequent Itemset; Pharmacy; Sales Patterns
Pharmacies are shops that sell and mix medicines based on doctors’ prescriptions and trade medical goods. Apart from being a business actor, the pharmacy also plays a role in providing health services that are easily accessible to the public. The problem that often occurs in pharmacies selling drugs is that they are less than optimal in service to consumers. The habit of consumers buying more than one type of drug makes pharmacy staff slow in providing the drug due to the inaccurate layout of the drug. The FP-Growth method in Data Mining is a method that can provide a solution in determining drug sales patterns at pharmacies. The FP-Growth method is a method used in determining the data set that occurs most frequently together (Frequent Itemset). The research objective was to determine drug sales patterns based on drug sales transaction data so that drug layouts could be determined. This research was conducted at the Pharmacy at the Pratama Sehati Husada Clinic in Medan with drug sales transaction data from November 2021 to December 2021. The results of applying the FP-Growth method with a value of ≥ 15% as a minimum support and a value of ≥ 15% as a minimum confidence, a pattern of drug sales in pairs. The application of Data Mining with the FP-Growth method has been implemented at the Pharmacy at the Pratama Sehati Husada Clinic with the desired goals with the final result in the form of a report on the results of determining drug sales patterns.
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