Implementasi Algoritma Apriori Dalam Keterkaitan Data Pada Kelangkaan Minyak Goreng

Abstract

The shortage of edible oil has caused the price of oil to be significantly higher than normal, which has led to higher oil prices even in certain regions where there are no edible oil inventories at all (empty warehouses). Due to the phenomenon of cooking oil shortages, many traders sell at high prices using correlation techniques, but they are not suitable. Given so many incidents where traders were selling cooking oil with related technology but not suited to consumers' needs, the authors became interested in analyzing the data connections by applying the a priori algorithm so that the goods paired with the cooking oil were not more useless and can be beneficial to buyers. Transaction data as a reference by a correlation by setting a cut-off value of 15% resulting from scanned transaction data, when buying cooking oil, often from 210 samples at the same time, and the samples taken have too good a number to be used as a correlation to become as they have a leverage value > 1.00 so that they can be used as a reference to arrange the cooking oil supply packaging along with other items.