Open AccessData Mining Penjualan Produk Dengan Metode Apriori Pada Indomaret Galang Kota
Author(s) -
Sheih Al Syahdan,
Anita Sindar
Publication year - 2018
Publication title -
jurnal nasional komputasi dan teknologi informasi/jurnal nasional komputasi dan teknologi informasi
Language(s) - English
Resource type - Journals
eISSN - 2621-3052
pISSN - 2620-8342
DOI - 10.32672/jnkti.v1i2.771
Subject(s) - association rule learning , database transaction , apriori algorithm , transaction data , computer science , affinity analysis , data mining , product (mathematics) , a priori and a posteriori , associative property , value (mathematics) , data warehouse , database , mathematics , machine learning , philosophy , geometry , epistemology , pure mathematics
The large number of transactions, companies need analytical tools to provide information that is useful for the company in determining the layout of goods, what items are most in demand by consumers and others. As experienced by several other supermarkets, product placement is a major problem. Data mining is a technique for digging up information that is hidden or hidden. This study will identify several types of association rules relating to sales transaction data, namely support and confidence values. The data used are 25 food and beverage products. Data mining technique uses associative rule with the Apriori method, aims to find a combination of items with a frequency pattern of the transaction results. After all high frequency patterns are found, then the association rules that meet the minimum requirements are found for confidence associative rules A → B minimum confidence = 25%, confidence value of A → B rules.