Implementasi Algoritma Apriori dan FP-Growth pada Penjualan Spareparts

Abstract

Data Mining can be applied in various areas, for example in PT. Agung Toyota Denpasar in order to increase sales and determine the sale of replacement parts. The current problem is to determine the replacement parts sale in PT. Agung Toyota Denpasar cannot know the purchasing habits of customers or customers in purchasing replacement parts purchased simultaneously. This research aims to implement apriori algorithms and fp-growth algorithms to form a model or a combination of rules so that businesses can increase their sales. Using the Knowledge Discovery Database (KDD) method should provide significant information on transaction patterns purchased simultaneously using the apriori and fp-growth algorithms. The dataset used to support this research is the sales transactional dataset for the period of January 2022. The results showed that the 10 best association rules of apriori algorithms and fp-growth algorithms were ready to be used to increase sales with a minimum support value of 85%, confidence value of 100%, and the highest lift ratio of 2.03.