Apriori Algorithm Analysis to Determine Purchasing Patterns at Beleven Farma Pharmacy
Abstract
Beleven Farma Pharmacy is a place that provides medicines and other health products such as supplements, vitamins and also various health tests. As a newly established pharmacy, no innovations have been made to improve sales strategies. Analysis of purchasing patterns can produce information that helps pharmacies in determining product bundling recommendations as well as determining product layout. This research applies the a priori algorithm method and uses rapidminer tools to identify drug purchasing patterns from transaction data at the Beleven Farma pharmacy. The Knowledge discovery in database (KDD) method is used as a reference in the data processing process. Based on tests carried out by the author, the resulting rules are that if you buy hemaviton you will buy vice with 4% support and 91% confidence and if you buy amoxicillin you will buy paracetamol with 4% support and 64% confidence. Thus, the resulting information can be used to support decision making in determining marketing strategies so as to increase sales at pharmacies.
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