IMPLEMENTATION OF APRIORI ALGORITHM WITH CUSTOMER ORDER PATTERN ANALYSIS FOR DETERMINATION OF RAW MATERIAL INVENTORY

Abstract

Ordering products from home-based trading businesses have not utilized data mining algorithms that can help analyze transaction data to optimize product order transactions and also manage inventory on raw materials from products by reducing a lot of leftover raw materials and products that are not purchased by customers. To avoid the occurrence of a lot of raw material leftovers from products that are not in demand and to find out which types of products are in demand by customers, then the apriori algorithm is needed. The purpose of this research is that the owner can carry out good and efficient management of the availability of raw materials from the product so that the raw materials can be processed into products, which must be adjusted to the number of products ordered by customers so that there is no accumulation of raw materials from fewer transactions. The results obtained from this research are if a customer orders product H, then he will order product L with a support value of 42% and 83% confidence, if a customer orders product L then he will order product G with a support value of 67% and a confidence of 90% -100%, and if a customer orders product H, he will order product G with a support value of 42% and 100% confidence. From the rules of this association, it can be concluded that this business owner can determine the management of raw materials by prioritizing the materials used to make products from L, H, and G so that the purchase and use of raw materials for these products are more manageable and efficient.