Open AccessConsumer purchase patterns based on market basket analysis using apriori algorithms
Author(s) -
Alon Efrat,
Rahmat Gernowo,
Farikhin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1524/1/012109
Subject(s) - apriori algorithm , purchasing , database transaction , transaction data , affinity analysis , a priori and a posteriori , computer science , consumer behaviour , value (mathematics) , consumer confidence index , database marketing , association rule learning , data mining , algorithm , marketing , database , business , machine learning , marketing management , relationship marketing , philosophy , epistemology
Analyzing customer purchasing patterns can help minimarket expand marketing strategies by gaining insight into which items are often bought together by customers. Also, transaction data is a source of information available at the convenience store and one thing that can be used for business decision making. In this paper, we aim to use the Apriori algorithm method to obtain consumer purchasing patterns to analyze consumer purchasing patterns. This system uses a priori algorithm calculation method where the input data is consumer transaction data. Transaction data will be sorted and calculated by providing a minimum support value and a minimum confidence value. In this study, the authors conclude that the results of the analysis of information systems in determining consumer purchasing patterns can be as information to determine sales and in the application of Apriori algorithms can provide information in the form of a combination of consumer purchase patterns based on consumer transaction data with a minimum support value above 10% and the minimum confidence value is above 65%.