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Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 1 (2020) · pp. 1–10

DOI
10.22146/ijccs.37415
ISSN
2460-7258, 1978-1520
Published
2020-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method. The method used in this research is RWO-sampling method using random replicate approach for synthetic data generation on descrete attribute. The result of the research can handle the problem of class imbalance, RWO-sampling method with random replicate approach shows better accuracy than RWO-sampling method with roulette and ROS approach. The accuracy value for RWO-Sampling method with roulette and RWO-Sampling approach with random replicate approach has increased to an average of 15.55% of each dataset. As for comparithem with the ROS method has increased an average of 3.7% of each dataset. Furthermore, for testing the underfitting problem in logistic regression, the oversampling method is better than non-oversampling with an increase in accuracy value reaching an average of 2.3% of each dataset.

Keywords

Collaboration network of the authors

20 authors · 31 co-authorship links · 8 across different organisations

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

  • Universitas Gadjah Mada
  • University of Brawijaya
  • National Research and Innovation Agency
  • Sumatera Institute of Technology
  • Universitas Negeri Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arief IchwaniSumatera Institute of Technology1
Devi Valentino WaasUniversitas Gadjah Mada1
Muhammad Faqih DzulqarnainUniversitas Gadjah Mada1
Faizal MakhrusUniversitas Gadjah Mada1
Wahyono WahyonoUniversitas Gadjah Mada1
Yaqutina Marjani SantosaUniversitas Gadjah Mada1
Agung PambudiUniversitas Gadjah Mada1
Nasution NasutionUniversitas Gadjah Mada1
Yusi Tyroni MursityoUniversity of Brawijaya1
Muhammad GhiffariUniversity of Brawijaya1
Yurizka Melia SariUniversitas Negeri Surabaya1
Janu ArlinwibowoNational Research and Innovation Agency1
Gupita Nadindra FatimaAffiliation unknown1
Dwi PurwokoNational Research and Innovation Agency1
Yufika Sari BagiAffiliation unknown1
San Afri AwangAffiliation unknown1
Ahmad MaryudiAffiliation unknown1
Wahyu WardhanaAffiliation 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">Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm</dc:title>
	<dc:creator>Ustyannie, Windyaning</dc:creator>
	<dc:creator>Suprapto, Suprapto</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Imbalanced Datasets; RWO-Sampling; Logistic Regression</dc:subject>
	<dc:description xml:lang="en-US">The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method. The method used in this research is RWO-sampling method using random replicate approach for synthetic data generation on descrete attribute. The result of the research can handle the problem of class imbalance, RWO-sampling method with random replicate approach shows better accuracy than RWO-sampling method with roulette and ROS approach. The accuracy value for RWO-Sampling method with roulette and RWO-Sampling approach with random replicate approach has increased to an average of 15.55% of each dataset. As for comparithem with the ROS method has increased an average of 3.7% of each dataset. Furthermore, for testing the underfitting problem in logistic regression, the oversampling method is better than non-oversampling with an increase in accuracy value reaching an average of 2.3% of each dataset.</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>2020-01-31</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/37415</dc:identifier>
	<dc:identifier>10.22146/ijccs.37415</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 1 (2020): January; 1-10</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/37415/26952</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 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>
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