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Predicting Analysis of User’s Interest from Web Log Data in e-Commerce using Classification Algorithms

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 15 No. 1 (2022) · pp. 33–38

DOI
10.21609/jiki.v15i1.1024
ISSN
2502-9274, 2088-7051
Published
2022-02-27
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

The accelerated development of e-commerce has been a concern for business people. Business people 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.

Keywords

Collaboration network of the authors

12 authors · 24 co-authorship links · 12 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Teknologi Yogyakarta
  • Universitas Gadjah Mada
  • AGH University of Krakow
  • Mulawarman University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Adhistya Erna PermanasariUniversitas Gadjah Mada1
Indriana HidayahUniversitas Gadjah Mada1
Enny Itje SelaUniversitas Teknologi Yogyakarta1
Briyan Efflin SyahputraUniversitas Teknologi Yogyakarta1
Nathaniela Aptanta ParamaUniversitas Teknologi Yogyakarta1
Anindita SeptiariniMulawarman University1
Shoffan SaifullahAGH University of Krakow1
Suhirman SuhirmanUniversitas Teknologi Yogyakarta1
M. Apriandi KusumaUniversitas Teknologi Yogyakarta1
Rafał DreżewskiAGH University of Krakow1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Predicting Analysis of  User’s Interest from Web Log Data in e-Commerce using Classification Algorithms</dc:title>
	<dc:creator>Diwandari, Saucha</dc:creator>
	<dc:creator>Hidayat, Ahmad Tri</dc:creator>
	<dc:subject xml:lang="en-US">web usage mining</dc:subject>
	<dc:subject xml:lang="en-US">web log data</dc:subject>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">user interest</dc:subject>
	<dc:subject xml:lang="en-US">clickstream data</dc:subject>
	<dc:description xml:lang="en-US">The accelerated development of e-commerce has been a concern for business people. Business people 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.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2022-02-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1024</dc:identifier>
	<dc:identifier>10.21609/jiki.v15i1.1024</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 15 No. 1 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 33-38</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v15i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1024/453</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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