Open AccessComparison of Classification Performance Based on Dynamic Mining of User Interest Navigation Pattern in e-Commerce Websites
Author(s) -
Saucha Diwandari,
Ahmad Taufiq Hidayat
Publication year - 2021
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 - 10.1088/1742-6596/1844/1/012025
Subject(s) - visitor pattern , web mining , computer science , classifier (uml) , decision tree , e commerce , data mining , competition (biology) , product (mathematics) , decision tree learning , world wide web , data science , artificial intelligence , web page , mathematics , ecology , geometry , biology , programming language
The use of e-commerce in companies or other types of business has supported them to develop and correspondingly cope with business pressures of high levels of competition. More consumer information can be gathered based on the interactive nature of e-commerce technology. In an e-commerce competition, all information relating to consumer behavior, such as the knowledge of the visitor interests in a product marketed by e-commerce, is of value to e-commerce players. Users can use the Web Usage Mining techniques to explore these interests. This study aimed to compare three classification algorithms by using the dynamic mining approach of user interest navigation pattern. The results of the study showed that the Decision Tree Classifier performed optimally in both the unbalanced data and independent or dependent data models.