Melola Discovery

← Melola Discovery

Jurnal

Pemodelan Regresi Logistik Ordinal dengan Dispersi Efek Lokasi

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 2 (2023) · pp. 144–152

DOI
10.20956/ejsa.v4i2.12355
ISSN
2721-379X, 2721-3803
Published
2023-08-04
Publisher
Hasanuddin University
Language
en

Abstract

Logistic regression ordinal is a regression model that can explain the relationship between predictor variables in the form of categorical data or continuous data with response variable is more than two categories with a scale of measurement that is level or sequence. In ordinal logistic regression, the frequency of occurrence in each response category is often very different, so it will affect the model's accuracy. Therefore, this study will model ordinal logistic regression with a dispersion of location effects, then applied to the nutritional status data of toddler in 2019 at the Pekkae Puskesmas, Barru Regency. The results obtained show that the ordinal logistic regression model with the dispersion of location effects is better than the usual ordinal logistic regression model for predicting the nutritional status data for toddlers in 2019 at Pekkae Puskesmas, Barru Regency based on deviance values. The factors that influence the nutritional status of toddler based on TB/U are gender, age, and height.

Keywords

Collaboration network of the authors

29 authors · 84 co-authorship links

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

  • Sultan Ageng Tirtayasa University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Raupong RaupongAffiliation unknown3
Anisa AnisaSultan Ageng Tirtayasa University2
Anisa KalondengAffiliation unknown2
Mamik UtamiAffiliation unknown2
Widya Nauli Amalia PuteriAffiliation unknown1
Zhazha Alifkhamulki RamdhaniAffiliation unknown1
Samsul ArifinAffiliation unknown1
Bunga ApriliaAffiliation unknown1
Nirwan IlyasAffiliation unknown1
Dewi Rahma EnteAffiliation unknown1
Muhammad Jayzul UsrahAffiliation unknown1
AnisaAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Novilia JaoAffiliation unknown1
Fitri FitriAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
Hajratul Ashwad KAffiliation unknown1
Andi Kresna JayaAffiliation unknown1
Andi Ummi Melin Aicha AichaAffiliation unknown1
Muhammad FadilAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
A. Elisha WulandariAffiliation unknown1
Belia Nurul FajriAffiliation unknown1
Abdullah PannuAffiliation unknown1
Alfian Akbar VallarinoAffiliation unknown1
Anugrah Nur Isnaeni RahmanAffiliation 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) 
<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">Pemodelan Regresi Logistik Ordinal dengan Dispersi Efek Lokasi</dc:title>
	<dc:creator xml:lang="en">Budistiharah, Ainun Utari</dc:creator>
	<dc:creator xml:lang="en">Islamiyati, Anna</dc:creator>
	<dc:creator xml:lang="en">Thamrin, Sri Astuti</dc:creator>
	<dc:subject xml:lang="en">deviance</dc:subject>
	<dc:subject xml:lang="en">location effect dispersion</dc:subject>
	<dc:subject xml:lang="en">ordinal logistic regression</dc:subject>
	<dc:subject xml:lang="en">nutritional status of children under five</dc:subject>
	<dc:description xml:lang="en">Logistic regression ordinal is a regression model that can explain the relationship between predictor variables in the form of categorical data or continuous data with response variable is more than two categories with a scale of measurement that is level or sequence. In ordinal logistic regression, the frequency of occurrence in each response category is often very different, so it will affect the model's accuracy. Therefore, this study will model ordinal logistic regression with a dispersion of location effects, then applied to the nutritional status data of toddler in 2019 at the Pekkae Puskesmas, Barru Regency. The results obtained show that the ordinal logistic regression model with the dispersion of location effects is better than the usual ordinal logistic regression model for predicting the nutritional status data for toddlers in 2019 at Pekkae Puskesmas, Barru Regency based on deviance values. The factors that influence the nutritional status of toddler based on TB/U are gender, age, and height.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2023-08-04</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/12355</dc:identifier>
	<dc:identifier>10.20956/ejsa.v4i2.12355</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 2, Juli, 2023 : Estimasi; 144-152</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/12355/10205</dc:relation>
</oai_dc:dc>