Implementation of the apriori algorithm and the topsis method in determining sales patterns at mentaya hafiz furniture

Abstract

Indonesia's economic growth during the Covid-19 pandemic was not good due to low levels of public consumption. The government and MSME actors are trying to build the economy so that it can survive in all the conditions it faces. This study aims to find out how the implementation of the a priori algorithm and the TOPSIS method determine the sales pattern of a business. The analysis was carried out using two methods, namely the association rule using the a priori algorithm and the TOPSIS method as a decision support system. The analysis of the a priori algorithm produces an itemset of house frames, doors and windows as a combination that meets the 20% support value. Meanwhile, there are three association rules, namely if you buy house frames and doors, then buy windows with a confidence value of 100% and a lift ratio of 1.89. It's the same as the other 2 rules which produce a lift ratio value of more than 1, which means the rule is valid. In the analysis of the TOPSIS method, of the 22 alternatives it is known that alternative A14, namely windows, is ranked 1st as the product that sells the most, followed by alternative A13, namely doors. In addition, there are several products that have the same preference value so that they are also in the same rank, such as cafe chairs, stakes/stones and flower shelves then cafe tables and shoe racks.