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Rachmi Helfianur
Telkom University
Computer Science & IT

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E-Commerce Recommender System on the Shopee Platform Using Apriori Algorithm Rachmi Helfianur; ZK Abdurahman Baizal
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.650

Abstract

The development of E-Commerce continues to increase every year, and all online shopping platforms continue to increase competition. Shopee as an online shopping platform offers various product categories that users need. To make it easier for users when shopping online, it is necessary to implement a product recommender system in E-Commerce. Therefore, in this study, we will build a recommender system using the a priori algorithm. The apriori algorithm is very widely used to find out the buying pattern of each user by looking at a combination of itemset. many recommender systems in e-commerce use various methods used, and provide recommendation results that display popular products, and based on the query results obtained. From the results of previous studies, there are similarities between products that have been liked by customers, so they do not have the best recommendations. Therefore, in this study we apply an apriori algorithm to add user confidence to the given recommendations, and to avoid overspecialization. In this research, we take the domain of electronics goods. In this study, the system produces the best value for association rules with a support value of 0.01, confidence 1.00, and lift 97.35.
Co-Authors ZK Abdurahman Baizal