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Predicting Students Graduate on Time Using C4.5 Algorithm

Journal of Information Systems Engineering and Business Intelligence · Vol. 7 No. 1 (2021) · pp. 67–73

DOI
10.20473/jisebi.7.1.67-73
ISSN
2443-2555, 2598-6333
Published
2021-04-27
Publisher
Universitas Airlangga
Language
eng

Abstract

Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students' on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students' graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.

Collaboration network of the authors

14 authors · 27 co-authorship links · 5 across different organisations

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

  • Universitas Ahmad Dahlan
  • Universitas Harapan Bangsa
  • Institut Teknologi Telkom Purwokerto
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhammad Kunta BiddinikaUniversitas Ahmad Dahlan2
Ainunna’imahUniversitas Ahmad Dahlan1
Imam RiadiUniversitas Ahmad Dahlan1
Bella Okta Sari MirandaUniversitas Ahmad Dahlan1
Dewi SoyusiawatyUniversitas Ahmad Dahlan1
Farhan Radhiansyah RazakUniversitas Ahmad Dahlan1
Herman YuliansyahUniversitas Ahmad Dahlan1
Kintung PrayitnoAffiliation unknown1
SunardiAffiliation unknown1
Kintung PrayitnoUniversitas Ahmad Dahlan1

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://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Predicting Students Graduate on Time Using C4.5 Algorithm</dc:title>
	<dc:creator>Yuliansyah, Herman</dc:creator>
	<dc:creator>Imaniati, Rahmasari Adi Putri</dc:creator>
	<dc:creator>Wirasto, Anggit</dc:creator>
	<dc:creator>Wibowo, Merlinda</dc:creator>
	<dc:description xml:lang="en-US">Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students' on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students' graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2021-04-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/25182</dc:identifier>
	<dc:identifier>10.20473/jisebi.7.1.67-73</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 7 No. 1 (2021): April; 67-73</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/25182/13994</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 The Authors. Published by Universitas Airlangga.</dc:rights>
</oai_dc:dc>