Sales Transaction Patterns Analysis in Pharmacy Using Frequent Pattern Growth Algorithm Located at Duren Sawit Dian Firdiansyah (a*), Yaddarabullah (a), Budi Arifitama (a)
(a) Program Studi Teknik Informatika, Universitas Trilogi
Jl.TMP Kalibata no.1, Jakarta 12760, Indonesia
*dianfirdiansyah[at]trilogi.ac.id
Abstract
The pharmaceutical industry has a vast number of data that can be cultivated and analyzed to produce valuable information. Sales transaction analysis aims to design an efficient sales strategy by utilizing drug sales transaction data. However, the pharmacy studied in this research has a difficulty to determine the type of medicine that is often purchased by customers, even though past purchasing data are available. This research applied the Frequent Pattern Growth algorithm to analyze transaction data at the pharmacy. The Frequent Pattern Growth algorithm provides transaction data into the frequent pattern-tree structure, generates a conditional pattern base, then presents a conditional pattern tree and frequent itemset is performed. The result of this research is a drug recommendation that has a minimum support value of 10% and a minimum confidence value of 50% with several transactions 25 and 50, with the result of a combination of rules between Termorex and Vitamin B Complex. These results can be implemented as a strategy for drug sales and management at the pharmacy.
Keywords: Frequent Pattern Growth Algorithm, Drug Sales Transaction, Drug Stock Recommendation
