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Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining

Journal of ICT Research and Applications (JICTRA) · Vol. 12 No. 1 (2018) · pp. 54–69

DOI
10.5614/itbj.ict.res.appl.2018.12.1.4
ISSN
2338-5499, 2337-5787
Published
2018-04-30
Publisher
DRI - ITB
Language
eng

Abstract

Visitor interaction with e-commerce websites generates large amounts of clickstream data stored in web access logs. From a business standpoint, clickstream data can be used as a means of finding information on user interest. In this paper, the authors propose a method to find user interest in products offered on e-commerce websites based on web usage mining of clickstream data. In this study, user interest was investigated using the PIE approach coupled with clustering and classification techniques. The experimental results showed that the method is able to assist in analyzing visitor behavior and user interest in e-commerce products by identifying those products that prompt visitor interest.

Keywords

Collaboration network of the authors

13 authors · 23 co-authorship links · 10 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
  • Institut Teknologi Telkom Purwokerto
  • Mulawarman University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nur Annisa TresnasariUniversitas Gadjah Mada1
Teguh Bharata AdjiUniversitas Gadjah Mada1
Ahmad Tri HidayatUniversitas Teknologi Yogyakarta1
Enny Itje SelaUniversitas Teknologi Yogyakarta1
Briyan Efflin SyahputraUniversitas Teknologi Yogyakarta1
Nathaniela Aptanta ParamaUniversitas Teknologi Yogyakarta1
Anindita SeptiariniMulawarman University1
Sri Suning KusumawardaniUniversitas Gadjah Mada1
Emi IryantiInstitut Teknologi Telkom Purwokerto1
La Ode Mohamad ZulfiqarInstitut Teknologi Telkom Purwokerto1

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://journals.itb.ac.id/index.php/jictra/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">Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining</dc:title>
	<dc:creator>Diwandari, Saucha</dc:creator>
	<dc:creator>Permanasari, Adhistya Erna</dc:creator>
	<dc:creator>Hidayah, Indriana</dc:creator>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">clickstream</dc:subject>
	<dc:subject xml:lang="en-US">data mining</dc:subject>
	<dc:subject xml:lang="en-US">e-commerce</dc:subject>
	<dc:subject xml:lang="en-US">user interest</dc:subject>
	<dc:subject xml:lang="en-US">web usage mining</dc:subject>
	<dc:subject xml:lang="en-US">web access log.</dc:subject>
	<dc:description xml:lang="en-US">Visitor interaction with e-commerce websites generates large amounts of clickstream data stored in web access logs. From a business standpoint, clickstream data can be used as a means of finding information on user interest. In this paper, the authors propose a method to find user interest in products offered on e-commerce websites based on web usage mining of clickstream data. In this study, user interest was investigated using the PIE approach coupled with clustering and classification techniques. The experimental results showed that the method is able to assist in analyzing visitor behavior and user interest in e-commerce products by identifying those products that prompt visitor interest.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2018-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/1921</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2018.12.1.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 12 No. 1 (2018); 54-69</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2018.12.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/1921/3253</dc:relation>
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