Implementation of the Association Rule Method using Apriori Algorithm to Recognize The Purchase Pattern of Pharmacy Drugs “XYZ”

Abstract

XYZ Pharmacy is a Special Health Service Point for employees and retirees of the XYZ company. This pharmacy carries out the process of buying and selling drugs by providing various types of drugs. The number of sales transactions in each day, resulting in sales data will increase over time. If the data is left alone, the pile of data will only become archives that are not utilized. By carrying out the data mining process, this data can be used to produce information that can be used to increase sales transactions at XYZ Pharmacy. The method used in this study is the Association Rule which functions to analyze the most sold and purchased drugs simultaneously, this analysis will be reviewed from drug sales transaction data at the XYZ Pharmacy. The application of the a priori algorithm in this study succeeded in finding the most item combinations based on transaction data and then formed an association pattern from the item combinations. By knowing the types of drugs that are often purchased together through identification of purchasing patterns, it is very useful for the XYZ Pharmacy to maintain the availability of the drugs.