Analisis Penjualan Produk Menggunakan Algoritma K-Means dan Apriori
DOI:
https://doi.org/10.33379/gtech.v8i2.4255Keywords:
assocation rule, apriori, clustering, k-means, salesAbstract
Distro Sextor is an SME industry in the clothing and fashion sector which is experiencing a decline in sales due to the influence of the regional economic slowdown and the impact of the pandemic. Therefore, a strategy is needed to increase sales based on information technology. The use of data mining with clustering and association rule techniques can help increase sales by finding patterns related to sales transactions. This analysis uses the K-Means algorithm for clustering and produces 5 clusters, namely clusters 0,1,2,3,4. The cluster chosen is cluster 0 which has the highest number of transactions with product categories that are cheaper and the types of products that are most frequently sold are t-shirts, shorts, trousers, and others. This analysis is continued with the association rule technique which uses an a priori algorithm to determine the relationship between products in transactions. The results of this analysis show 5 rules and 7 products that can be recommended as promotional media, namely SE130, SE111, SE128, SE126, SE04, SE40, and SE11.
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