Implementasi Data Mining Pada Sistem Persediaan Barang Menggunakan Algoritma Apriori (Studi Kasus: Srikandi Cash Credit Elektronic dan Furniture)

Abstract

Inventories of goods that are not carried out optimally will cause a vacancy for one of the available goods. This also happens to electronic credit and electronic credit shops, which often results in a vacuum in one of the items purchased by the customer, as a result of the lack of information regarding inventory control habits. So it is necessary to extract information on transaction data. A priori algorithm can help to find out the name of the item with the most sales. A priori algorithm including the type of association rules in data mining, an association can be known with two benchmarks, namely support and confidence. Support (supporting value) is the percentage of combinations of these items, while confidance is the relationship between items in association rules. Proof is carried out using the Tanagra application. The results obtained from the a priori algorithm process in the form of a combination of items or rules with the association value in the form of support and confidence values. By knowing the name of the goods most sold, it can anticipate the inventory of goods.