ANALISIS POLA PEMBELIAN KONSUMEN MENGGUNAKAN ALGORITMA APRIORI

Abstrak

In order for sales transaction data to be useful and useful, data development or data mining methods are needed, especially in the use of the apriori association rule algorithm, in utilizing sales transaction data obtained from items purchased simultaneously by consumers when shopping. The data is used to find out items that are often purchased by consumers to increase their stock and reduce the stocking of goods that are not selling well. The data is also used by the store in arranging the placement of goods according to consumer behavior when shopping. Data retrieval with association rule is done through a mechanism with a minimum support of 0.17 and a minimum confidence of 0.60. The purpose of this study is to determine the results of analyzing transaction data that accumulates in clothing stores into useful information and to find out what items are bought simultaneously by consumers at clothing stores to manage inventory and can make it easier to arrange the placement of goods according to consumer interests based on the value of support and confidence. From the results of this study, items that have a relationship are Women's Tops and Men's Tops with a support value of 27.86% and a confidence value of 68.00%. This information can also be used to find out what items are in great demand and less in demand by consumers in determining what stock procurement should be prioritized in the future.