Perancangan Aplikasi Data Mining Menggunakan Association Rule Dengan Metode Algoritma Apriori Untuk Analisis Market Basket (Studi Kasus Pada Tesco Swalayan) - Arman, Shevti Arbekti; Terisia, Vany; Toha, Muhamad (ITB Ahmad Dahlan Jakarta, 2021)
The data contained ranging from business transactions to government is very large in size so we need a system that can sort and select the data. Intense competition in the business world, especially in the food industry or supermarkets, requires developers to find strategies that can increase sales. Data mining is used to find patterns and regularities in data sets that are hidden by using technology. In knowing the products purchased by consumers, developers can use market basket analysis, namely an analysis of consumer buying habits and then detect products that consumers often buy simultaneously using association rules. Search for associations or relationships between items taken from a relational database using an a priori algorithm to form candidate item combinations and then tested whether the combination meets the minimum support and confidence parameters which are the threshold values ​​given by the user. Furthermore, it is applied by performing query processing, decision making and so on. Data mining applications with the association rule method have been able to perform calculations correctly as expected. The knowledge generated by the application and manual calculations is 17.2% of all transactions, granulated sugar and pomegranate cooking oil are purchased simultaneously. And of all transactions that buy granulated sugar, 55.5% will buy Pomegranate Cooking Oil. 17.2% of all transactions, Pomegranate Cooking Oil and Granulated Sugar are purchased simultaneously. And of all transactions that buy Pomegranate Cooking Oil, 55.5% will buy granulated sugar.