Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 3

ESTIMATION OF MAXIMUM LIKELIHOOD WEIGHTED LOGISTIC REGRESSION USING GENETIC ALGORITHM (CASE STUDY: INDIVIDUAL WORK STATUS IN MALANG CITY)

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 1 (2023) · pp. 0487–0494

DOI
10.30598/barekengvol17iss1pp0487-0494
ISSN
2615-3017, 1978-7227
Published
2023-04-20
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Weighted Logistic Regression (WLR) is a method used to overcome imbalanced data or rare events by using weighting and is part of the development of a simple logistic regression model. Parameter estimation of the WLR model uses Maximum Likelihood estimation. The maximum likelihood parameter estimator value is obtained using an optimization approach. The Genetic algorithm is an optimization computational algorithm that is used to optimize the estimation of model parameters. This study aims to estimate the Maximum Likelihood Weighted Logistic Regression with the applied genetic algorithm and determine the significant variables that affect the working status of individuals in Malang City. The data used is the result of data collection from the National Labor Force Survey of Malang City in 2020. The results of the analysis show that the variable education completed and the number of household members has a significant effect on individual work status in Malang City.

Collaboration network of the authors

28 authors · 84 co-authorship links

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

  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Henny PramoedyoUniversity of Brawijaya2
Rinda Lolita MelanwatiUniversity of Brawijaya1
Dwi Ayu LusiaUniversity of Brawijaya1
Karen SematheaAffiliation unknown1
Achmad EfendiAffiliation unknown1
Karen SematheaAffiliation unknown1
Achmad EfendiAffiliation unknown1
Ni Wayan Surya WardhaniUniversity of Brawijaya1
Atiek IrianyUniversity of Brawijaya1
Nabila Ayunda SoviaAffiliation unknown1
Ani Budi AstutiUniversity of Brawijaya1
Wigbertus NgabuAffiliation unknown1
Suci AstutikUniversity of Brawijaya1
Adji Achmad Rinaldo FernandesUniversity of Brawijaya1
Armando Jacquis Federal ZamelinaAffiliation unknown1
Lucius RamifidisoaAffiliation unknown1
Solimun SolimunUniversity of Brawijaya1
Hafizh Syihabuddin Al JauharAffiliation unknown1
Naufal Shela AbdilaAffiliation unknown1
Muhamad Liswansyah PratamaAffiliation unknown1
Hugo WaramiAffiliation unknown1
Jonni MarwaAffiliation unknown1
Trigarcia Maleachi RandaAffiliation unknown1
Chrisaria PalunganAffiliation unknown1
Yusuf Willem SawakiAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">ESTIMATION OF MAXIMUM LIKELIHOOD WEIGHTED LOGISTIC REGRESSION USING GENETIC ALGORITHM  (CASE STUDY: INDIVIDUAL WORK STATUS IN MALANG CITY)</dc:title>
	<dc:creator>Menufandu, Dahlia Gladiola Rurina</dc:creator>
	<dc:creator>Fitriani, Rahma</dc:creator>
	<dc:creator>Sumarminingsih, Eni</dc:creator>
	<dc:description xml:lang="en-US">Weighted Logistic Regression (WLR) is a method used to overcome imbalanced data or rare events by using weighting and is part of the development of a simple logistic regression model. Parameter estimation of the WLR model uses Maximum Likelihood estimation. The maximum likelihood parameter estimator value is obtained using an optimization approach.&amp;nbsp; The Genetic algorithm is an optimization computational algorithm that is used to optimize the estimation of model parameters. This study aims to estimate the Maximum Likelihood Weighted Logistic Regression with the applied genetic algorithm and determine the significant variables that affect the working status of individuals in Malang City. The data used is the result of data collection from the National Labor Force Survey of Malang City in 2020. The results of the analysis show that the variable education completed and the number of household members has a significant effect on individual work status in Malang City.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-04-20</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7633</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss1pp0487-0494</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0487-0494</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss1year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7633/5567</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Dahlia Gladiola Rurina Menufandu, Rahma Fitriani, Eni Sumarminingsih</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>