Analisis Data Mining Produk Retail Menggunakan Metode Asosiasi Dengan Menerapkan Algoritma Apriori

Abstract

Retail products are businesses that use association techniques that apply a priori algorithms that retrieve datasets from Github in the form of csv taken online that look for the confidence value of these items by having a minimum support value according to these items taken from various countries 4 countries for analysis. The purpose of this research is to find out the pattern of association which aims to find the greatest value taken from 4 countries according to each item using a priori analysis to find out what is related to the data it has as many as 541,910 purchases of retail products by consumers in the form of a dataset that I get the data via online from github in csv form using jypter notebook. The Apriori algorithm is expected to provide decision support between goods purchased jointly by customers. Data Mining is a process that orders one or more learning using Association Rules which serves descriptive data mining which aims to find associative rules between data items. The main step that needs to be in the association rules is to find out how often item combinations appear in the database, which are often referred to as frequent patterns, to obtain a confidence value to find the minimum support value according to each country.