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Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 11 No. 2 (2018) · pp. 67–76

DOI
10.21609/jiki.v11i2.536
ISSN
2502-9274, 2088-7051
Published
2018-06-29
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Bidikmisi scholarship grantees are determined based on criteria related to the socioeconomic conditions of the parent of the scholarship grantee. Decision process of Bidikmisi acceptance is not easy to do, since there are sufficient big data of prospective applicants and variables of varied criteria. Based on these problems, a new approach is proposed to determine Bidikmisi grantees by using the Bayesian Bernoulli mixture regression model. The modeling procedure is performed by compiling the accepted and unaccepted cluster of applicants which are estimated for each cluster by the Bernoulli mixture regression model. The model parameter estimation process is done by building an algorithm based on Bayesian Markov Chain Monte Carlo (MCMC) method. The accuracy of acceptance process through Bayesian Bernoulli mixture regression model is measured by determining acceptance classification percentage of model which is compared with acceptance classification percentage of the dummy regression model and the polytomous regression model. The comparative results show that Bayesian Bernoulli mixture regression model approach gives higher percentage of acceptance classification accuracy than dummy regression model and polytomous regression model

Keywords

Collaboration network of the authors

12 authors · 36 co-authorship links · 12 across different organisations

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

  • Sebelas Maret University
  • Institut Teknologi Sepuluh Nopember
  • Universitas Muhammadiyah Surabaya
  • Padjadjaran University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Titis Jati NugrahaSebelas Maret University1
Winita SulandariSebelas Maret University1
İsnandar SlametSebelas Maret University1
Sri SubantiSebelas Maret University1
Etik ZukhronahSebelas Maret University1
Sugianto SugiantoSebelas Maret University1

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://jiki.cs.ui.ac.id/index.php/jiki/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification</dc:title>
	<dc:creator>Iriawan, NUR</dc:creator>
	<dc:creator>Fithriasari, Kartika</dc:creator>
	<dc:creator>Ulama, Brodjol Sutija Suprih</dc:creator>
	<dc:creator>Suryaningtyas, Wahyuni</dc:creator>
	<dc:creator>Susanto, Irwan</dc:creator>
	<dc:creator>Pravitasari, Anindya Apriliyanti</dc:creator>
	<dc:subject xml:lang="en-US">Bernoulli mixture regression model</dc:subject>
	<dc:subject xml:lang="en-US">Bayesian MCMC</dc:subject>
	<dc:subject xml:lang="en-US">Gibbs Sampling</dc:subject>
	<dc:subject xml:lang="en-US">Bidikmisi</dc:subject>
	<dc:description xml:lang="en-US">Bidikmisi scholarship grantees are determined based on criteria related to the socioeconomic conditions of the parent of the scholarship grantee. Decision process of Bidikmisi acceptance is not easy to do, since there are sufficient big data of prospective applicants and variables of varied criteria. Based on these problems, a new approach is proposed to determine Bidikmisi grantees by using the Bayesian Bernoulli mixture regression model. The modeling procedure is performed by compiling the accepted and unaccepted cluster of applicants which are estimated for each cluster by the Bernoulli mixture regression model. The model parameter estimation process is done by building an algorithm based on Bayesian Markov Chain Monte Carlo (MCMC) method. The accuracy of acceptance process through Bayesian Bernoulli mixture regression model is measured by determining acceptance classification percentage of model which is compared with acceptance classification percentage of  the dummy regression model and the polytomous regression model. The comparative results show that Bayesian Bernoulli mixture regression model approach gives higher percentage of acceptance classification accuracy than dummy regression model and polytomous regression model</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2018-06-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/536</dc:identifier>
	<dc:identifier>10.21609/jiki.v11i2.536</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 11 No. 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 67-76</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v11i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/536/401</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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