APPLICATION OF DATA MINING IN UNDERSTANDING MEDICINE PURCHASE TRANSACTION PATTERNS USING THE APRIORI ALGORITHM AT THE KHARISMA FARMA TIGA PHARMACY
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Prastyadi Wibawa Rahayu(1*)
Universitas Dhyana Pura
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I Nyoman Bernadus(2)
Universitas Dhyana Pura
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Aulia Iefan Datya(3)
Universitas Dhyana Pura
(*) Corresponding Author
Abstract
Drug storage at the Kharisma Farma Tiga Pharmacy is grouped based on class and form of availability, for categories namely patented, external, over-the-counter, herbal, generic and syrup drugs while for forms of availability, namely drugs in the form of bottles, syrups, creams, capsules, ointments, injection fluids, solutions, roll on and drip. Grouping of drugs based on class and availability in alphabetical order. However, there is a problem when several customers come to the pharmacy at the same time to buy various types of medicines based on different classes or availability of medicines, resulting in a lack of time efficiency in service, pharmacy staff need additional time to search for medicines in their storage place. Seeing that the storage of medicines at the Kharisma Farma Tiga pharmacy is still sorted alphabetically based on class and availability, we need an application that can help recommend arranging the layout of medicine storage by reading customer drug purchase transaction patterns at the Kharisma Farma Tiga Pharmacy so that the storage process medicines according to medicines that are often purchased simultaneously and can increase time efficiency in better customer service, especially in searching for medicines. The application of data mining to determine drug purchasing patterns using the Apriori algorithm at the Kharisma Farma Tiga Pharmacy was built on a web basis. The results obtained in this research are that the application of an a priori algorithm to determine the pattern of drug purchase transactions at the Kharisma Farma Tiga pharmacy has been successfully carried out and can provide recommendations for the layout of drugs that are often purchased based on drug purchases, as for the association rules obtained with a minimum support of 20% and confidence. 75%, namely 2 item sets with 4 rules and 3 item sets with 7 rules. The highest confidence value in the 2 item set is if the consumer buys 60 ml GPU oil then buys 10 ml of capax wind oil, while in the 3 item set is if the consumer buys redoxon effervescent 20s, 60 ml GPU oil then buys 10 ml of capax wind oil.
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