Open AccessIMPLEMENTASI DATA MINING UNTUK MENGETAHUI POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI
Author(s) -
Nadya Febrianny Ulfha,
R. R. Amin
Publication year - 2020
Publication title -
komputasi
Language(s) - English
Resource type - Journals
eISSN - 2654-3990
pISSN - 1693-7554
DOI - 10.33751/komputasi.v17i2.2156
Subject(s) - apriori algorithm , association rule learning , database transaction , transaction data , purchasing , business , value (mathematics) , marketing , pharmacy , a priori and a posteriori , data mining , advertising , computer science , database , medicine , machine learning , philosophy , family medicine , epistemology
Competition in the business world requires entrepreneurs to think of finding a way or method to increase the transaction of goods sold. The purpose of this research is to provide drug stock data that is widely purchased by pharmacy customers at Kimia Farma, Green Lake branch in Jakarta. The algorithm used in this study is a priori to determine the relationship between the frequency of sales of drug brands most frequently purchased by customers. The association pattern formed with a minimum support of 40% and a minimum value of 70% confidence produces 17 association rules. The strong rules obtained are that if you buy a 500Mg Ponstan KPL @ 100, you will buy an Incidal OD 10Mg Cap with a support value of 59% and a confidence value of 84%. A priori algorithm can be used by companies to develop marketing strategies in marketing products by examining consumer purchasing patterns.