Market Basket Analysis Using Apriori Algorithm to Determine Item Layout (Case Study: Mooladhara Pharmacy Denpasar)
DOI:
https://doi.org/10.33379/gtech.v7i2.2404Keywords:
Market Basket Analysis, Apriori Algorithm, Data Mining, Pharmacy TransactionsAbstract
Pharmacies play a crucial role in maintaining the quality of public health by creating good-quality pharmaceutical services. The quality of pharmaceutical services can be assessed in several ways, one of which is the speed of pharmaceutical services. Mooladhara Pharmacy is a pharmacy in Denpasar, Bali, which has problems with the speed of pharmacy services. Mooladhara Pharmacy sometimes takes a long time to serve customers who buy more than one drug because of difficulty finding it. Data mining is the search for information from large datasets to discover hidden patterns. The Apriori algorithm is one of the data mining algorithms for conducting market basket analysis to obtain customer shopping patterns. This study applies data mining to 581 transaction data to solve Mooladhara Pharmacy's problem. The results discover several customer transaction patterns. For example, a customer who buys Mefenamic Acid 500 Mg has a 65.2% chance of buying Amoxicillin 500 Mg (HJ).
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