IMPLEMENTASI ALGORITMA MAXIMAL FREQUENT PATTERNS UNTUK MENGANALISIS POLA PEMBELIAN OBAT (STUDI KASUS: RSUD ARIFIN ACHMAD)

Abstract

Drug purchase transaction data are very valuable treasure in sales. Drug purchase transaction data are used to generate new knowledge in the transaction database. Drug purchase transaction data can be managed using the data mining association rules method. The aim of the study was to apply maximal frequent patterns algorithms to analyze a patient's drug purchase patterns, look for drug combinations purchased simultaneously by patients, apply the fp-max association rules algorithm to get rules by testing the minimum support and minimum confidence desired, and knowing what variables can affect the association. Drug purchase transaction data were processed by the data mining method association rule technique using the fp-max algorithm. Drug purchase transaction data were tested as much as 1909 data with a minimum support of 1% and minimum confidence of 20% which resulted in 8 rules with a lift ratio value of 1 as many as 2 rules. While testing using a minimum support of 2% and so on does not produce rules because no frequent itemset were produced. Testing lift ratio on 8 rules concluded no value is worth more than 1, meaning there were no rules that showed a strong dependence between antacedent and consequence so it can not be used as a recommendation or prediction of the emergence of a drug due to the emergence of other drugs. The amount of support count value affected the number of frequent items formed and the high minimum confidence limit affects the rules produced