Analisis Data Transaksi Penjualan Obat Menggunakan Algortima Apriori Pada Kimia Farma Batam
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Abstract
With the advancement of information technology, there are many opportunities for the business sector, including the pharmaceutical industry, to use data to make strategic decisions. The Apriori algorithm, which falls under the category of association rule mining data mining techniques, is one of the effective methods for extracting hidden information from sales transaction data. The purpose of this study is to see the drug purchasing patterns that occur at the Kimia Farma Batam Pharmacy using the Apriori algorithm to help improve sales strategies, stock management, and customer service. The data used in this study comes from the Kimia Farma Batam pharmacy management information system. This data is collected from drug sales transactions during a certain period. The analysis begins with a data pre-processing stage, which includes cleaning and transforming the data. Then, the Apriori algorithm is used to extract association patterns. The results of the Apriori algorithm analysis show that drug combinations such as Ibuprofen and Paracetamol, and Aspirin and Paracetamol are often purchased together. This information can be used for bundling strategies, shelf arrangement, and cross-selling at Kimia Farma Batam pharmacies. With this research, Kimia Farma Batam Pharmacy can gain competitive advantage and improve the quality of data-based decision making with the Apriori algorithm that understands consumer behavior.
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