Predicting Analysis of User’s Interest from Web Log Data in e-Commerce using Classification Algorithms
Dublin Core
Title
Predicting Analysis of User’s Interest from Web Log Data in e-Commerce using Classification Algorithms
Subject
user interest; web usage mining; classification; e-commerce; web log data
Description
The accelerated development of e-commerce has been a concern for businesspeople. Businesspeople
should be able to gain customer interest in a variety of ways so that their companies can compete with
others. Analyzing click-flow data will help organizations or firms assess customer loyalty, provide advertising privileges, and develop marketing strategies through user interests. By understanding consumer preferences, clickstream data analysis may be used to determine who is participating, assist companies in evaluating customer contentment, boost productivity, and design marketing strategies. This research was performed by defining experimental user interests using Dynamic Mining and Page Interest Estimation methods. The findings of this analysis, using three algorithms at the pattern discovery page, demonstrated that the Decision Tree method excelled in both methods. It indicated that the operational performance of the Decision Tree performed well in the assessment of user interests
with two different approaches. The findings of this experiment can be used as a proposal for researching the field of web usage mining, collaborating with other approaches to achieve higher accuracy values.
should be able to gain customer interest in a variety of ways so that their companies can compete with
others. Analyzing click-flow data will help organizations or firms assess customer loyalty, provide advertising privileges, and develop marketing strategies through user interests. By understanding consumer preferences, clickstream data analysis may be used to determine who is participating, assist companies in evaluating customer contentment, boost productivity, and design marketing strategies. This research was performed by defining experimental user interests using Dynamic Mining and Page Interest Estimation methods. The findings of this analysis, using three algorithms at the pattern discovery page, demonstrated that the Decision Tree method excelled in both methods. It indicated that the operational performance of the Decision Tree performed well in the assessment of user interests
with two different approaches. The findings of this experiment can be used as a proposal for researching the field of web usage mining, collaborating with other approaches to achieve higher accuracy values.
Creator
Saucha Diwandari, Ahmad Tri Hidayat
Source
http://dx.doi.org/10.21609/jiki.v15i1.1024
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2022-07-02
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Saucha Diwandari, Ahmad Tri Hidayat, “Predicting Analysis of User’s Interest from Web Log Data in e-Commerce using Classification Algorithms,” Repository Horizon University Indonesia, accessed May 22, 2025, https://repository.horizon.ac.id/items/show/8837.