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Pemilihan Model Regresi Logistik Ordinal Terbaik Menggunakan Metode Stepwise: (Studi Kasus: Data Indeks Prestasi Kumulatif Lulusan Program Sarjana FMIPA Unmul)

ESTIMASI: Journal of Statistics and Its Application · Vol. 7 No. 1 (2026) · pp. 159–180

DOI
10.20956/ejsa.v7i1.44426
ISSN
2721-379X, 2721-3803
Published
2026-05-05
Publisher
Hasanuddin University
Language
en

Abstract

Ordinal logistic regression is one of the statistical methods used to model response variables with two or more categories that have levels. This study aims to model the cumulative grade point average data of undergraduate graduates of the Faculty of Mathematics and Natural Sciences, Mulawarman University in 2023 using ordinal logistic regression. Estimation of ordinal logistic regression model parameters is done using the Maximum Likelihood Estimation (MLE) method and Newton-Raphson iteration. The best model selection was conducted using the stepwise method based on the smallest Akaike Information Criterion (AIC) value and significant predictor variables. The selection process started with six predictor variables, then gradually eliminated three predictor variables because they were not significant and did not reduce the AIC value. The stepwise stage stopped at the model with three significant predictor variables that had an AIC value of 349,22. The results showed that the factors that had a significant effect on the cumulative grade point average of undergraduate graduates of the Faculty of Mathematics and Natural Sciences, Mulawarman University based on the best ordinal logistic regression model were study program, age, and admission pathway.

Keywords

Collaboration network of the authors

11 authors · 19 co-authorship links

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  • Monash University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Asrul SaniAffiliation unknown1
Muhammad SudiaAffiliation unknown1
Bambang IrawanMonash University1
Sumardi SumardiAffiliation unknown1
Rochmah AgustrinaAffiliation unknown1
Herta M. ManullangAffiliation unknown1
Eka Nur AmaliahAffiliation unknown1
Sifriyani SifriyaniAffiliation 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.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Pemilihan Model Regresi Logistik Ordinal Terbaik Menggunakan Metode Stepwise: (Studi Kasus: Data Indeks Prestasi Kumulatif Lulusan Program Sarjana FMIPA Unmul)</dc:title>
	<dc:creator xml:lang="en">Selsi, Selsi</dc:creator>
	<dc:creator xml:lang="en">Darnah, Darnah</dc:creator>
	<dc:creator xml:lang="en">Wahyuningsih, Sri</dc:creator>
	<dc:subject xml:lang="en">Grade Point Average</dc:subject>
	<dc:subject xml:lang="en">Stepwise Method</dc:subject>
	<dc:subject xml:lang="en">MLE</dc:subject>
	<dc:subject xml:lang="en">Newton-Raphson</dc:subject>
	<dc:subject xml:lang="en">Ordinal Logistic Regression</dc:subject>
	<dc:description xml:lang="en">Ordinal logistic regression is one of the statistical methods used to model response variables with two or more categories that have levels. This study aims to model the cumulative grade point average data of undergraduate graduates of the Faculty of Mathematics and Natural Sciences, Mulawarman University in 2023 using ordinal logistic regression. Estimation of ordinal logistic regression model parameters is done using the Maximum Likelihood Estimation (MLE) method and Newton-Raphson iteration. The best model selection was conducted using the stepwise method based on the smallest Akaike Information Criterion (AIC) value and significant predictor variables. The selection process started with six predictor variables, then gradually eliminated three predictor variables because they were not significant and did not reduce the AIC value. The stepwise stage stopped at the model with three significant predictor variables that had an AIC value of 349,22. The results showed that the factors that had a significant effect on the cumulative grade point average of undergraduate graduates of the Faculty of Mathematics and Natural Sciences, Mulawarman University based on the best ordinal logistic regression model were study program, age, and admission pathway.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2026-05-05</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://journal.unhas.ac.id/index.php/ESTIMASI/article/view/44426</dc:identifier>
	<dc:identifier>10.20956/ejsa.v7i1.44426</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 7, No. 1, Januari, 2026 : Estimasi; 159-180</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/44426/14396</dc:relation>
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