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Open access Jurnal SINTA 2

Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 1 (2024) · pp. 95–106

DOI
10.22146/ijccs.90437
ISSN
2460-7258, 1978-1520
Published
2024-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The field of information technology requires software, which has significant issues. Quality and reliability improvement needs damage prediction. Tree-based algorithms like Random Forest, Deep Forest, and Decision Tree offer potential in this domain. However, proper hyperparameter configuration is crucial for optimal outcomes. This study demonstrates the use of Random Search Hyperparameter Setting Technique to predict software defects, improving damage estimation accuracy. Using ReLink datasets, we found effective algorithm parameters for predicting software damage. Decision Tree, Random Forest, and Deep Forest achieved an average AUC of 0.73 with Random Search. Random Search outperformed other tree-based algorithms. The main contribution is the innovative Random Search hyperparameter tuning, particularly for Random Forest. Random Search has distinct advantages over other tree-based algorithms

Keywords

Collaboration network of the authors

29 authors · 96 co-authorship links · 2 across different organisations

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

  • Lambung Mangkurat University
  • Universitas Mercu Buana Yogyakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Radityo Adi NugrohoLambung Mangkurat University7
Rudy HertenoLambung Mangkurat University5
Muhammad Itqan MazdadiLambung Mangkurat University5
Andi FarmadiLambung Mangkurat University4
Reina Alya RahmaLambung Mangkurat University3
Setyo Wahyu SaputroLambung Mangkurat University2
Annisa Salsabila AhdyaniLambung Mangkurat University2
Deni KurniaLambung Mangkurat University2
Siti Roziana AzizahLambung Mangkurat University2
Salsha FarahdibaLambung Mangkurat University1
Triando Hamonangan SaragihLambung Mangkurat University1
Fenny Winda RahayuLambung Mangkurat University1
Dodon Turianto NugrahadiLambung Mangkurat University1
Muliadi MuliadiLambung Mangkurat University1
Sri RedjekiAffiliation unknown1
Fatma IndrianiLambung Mangkurat University1
Putri NabellaAffiliation unknown1
Putri NabellaLambung Mangkurat University1
Muhammad FikriUniversitas Mercu Buana Yogyakarta1
Radityo Adi NugrohoAffiliation unknown1
Setyo Wahyu SaputroAffiliation unknown1
Syarif MaulanaAffiliation unknown1
Dodon Turianto NugrahadiAffiliation unknown1
Mohammad Reza FaisalAffiliation unknown1

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://journal.ugm.ac.id/ijccs/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">Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction</dc:title>
	<dc:creator>Rizky, Muhammad Hevny</dc:creator>
	<dc:creator>Faisal, Mohammad Reza</dc:creator>
	<dc:creator>Budiman, Irwan</dc:creator>
	<dc:creator>Kartini, Dwi</dc:creator>
	<dc:creator>Abadi, Friska</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Software Defect Prediction; Hyperparameter Tuning; Decision Tree; Random Forest; Deep Forest;</dc:subject>
	<dc:description xml:lang="en-US">The field of information technology requires software, which has significant issues. Quality and reliability improvement needs damage prediction. Tree-based algorithms like Random Forest, Deep Forest, and Decision Tree offer potential in this domain. However, proper hyperparameter configuration is crucial for optimal outcomes. This study demonstrates the use of Random Search Hyperparameter Setting Technique to predict software defects, improving damage estimation accuracy. Using ReLink datasets, we found effective algorithm parameters for predicting software damage. Decision Tree, Random Forest, and Deep Forest achieved an average AUC of 0.73 with Random Search. Random Search outperformed other tree-based algorithms. The main contribution is the innovative Random Search hyperparameter tuning, particularly for Random Forest. Random Search has distinct advantages over other tree-based algorithms</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-01-31</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"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/90437</dc:identifier>
	<dc:identifier>10.22146/ijccs.90437</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 1 (2024): January; 95-106</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/90437/38507</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>