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CLASSIFICATION OF STUNTING USING GEOGRAPHICALLY WEIGHTED REGRESSION-KRIGING CASE STUDY: STUNTING IN EAST JAVA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 1 (2023) · pp. 0495–0504

DOI
10.30598/barekengvol17iss1pp0495-0504
ISSN
2615-3017, 1978-7227
Published
2023-04-20
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Geographically Weighted Regression Kriging (GWRK) is a special case of Geographically Weighted Regression (GWR) model, which is modeling with the effect of spatial autocorrelation on the GWR model error. The purpose of this research is to obtain a GWRK model between the factors that affect stunting density for each site viewed from the district center point in East Java Province and to make a prediction map based on the GWRK modeling. The data used was obtained from Basic Health Research (RISKESDAS) and the East Java Health Profile Book for 2021. The units of observation in this study were 38 districts in East Java.. Based on the GWR modeling results, it was found that the GWR model error contained spatial autocorrelation so that GWR model can be formed. From the GWRK modeling using stunting prevalence data in East Java in 2021, it was found that the GWR model was better than the global regression. Through prediction and prediction mapping formed from the GWR-Kriging modeling, it could be seen that stunting in regencies in East Java was evenly distributed. The interpolation map showed that the stunting forecasting values using the Kriging GWR interpolation ranged from 27% to 46%.

Collaboration network of the authors

31 authors · 106 co-authorship links

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  • 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
Adji Achmad Rinaldo FernandesUniversity of Brawijaya4
Solimun SolimunUniversity of Brawijaya3
Henny PramoedyoUniversity of Brawijaya3
Ani Budi AstutiUniversity of Brawijaya3
SolimunAffiliation unknown2
Dea Saraswati PramaningrumUniversity of Brawijaya2
Hanifa SepriadiAffiliation unknown2
Adji Achmad Rinaldo FernandesAffiliation unknown2
Ni Wayan Surya WardhaniUniversity of Brawijaya2
Danang AriyantoAffiliation unknown2
Sativandi RizaAffiliation unknown2
Adji FernandesAffiliation unknown1
Fachira Haneinanda JuniantoUniversity of Brawijaya1
Hanifa SepriadiUniversity of Brawijaya1
Cindy Rudiat SekarsariUniversity of Brawijaya1
Eni SumarminingsihUniversity of Brawijaya1
Nabila Ayunda SoviaAffiliation unknown1
Rahma FitrianiAffiliation unknown1
Abdi Negara GuciAffiliation unknown1
Viky Iqbal Azizul AlimAffiliation unknown1
Laila Nur AzizahAffiliation unknown1
Meirida Karisma PutriAffiliation unknown1
Ulfah MaisarohAffiliation unknown1
Suci AstutikUniversity of Brawijaya1
Petrus Kanisius OlaAffiliation unknown1
Devi Veda AmandaAffiliation unknown1
Maulidya MaghfiroAffiliation 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">CLASSIFICATION OF STUNTING USING GEOGRAPHICALLY WEIGHTED REGRESSION-KRIGING CASE STUDY: STUNTING IN EAST JAVA</dc:title>
	<dc:creator>Iriany, Atiek</dc:creator>
	<dc:creator>Ngabu, Wigbertus</dc:creator>
	<dc:creator>Arianto, Danang</dc:creator>
	<dc:creator>Putra, Arditama</dc:creator>
	<dc:description xml:lang="en-US">Geographically Weighted Regression Kriging (GWRK) is a special case of Geographically Weighted Regression (GWR) model, which is modeling with the effect of spatial autocorrelation on the GWR model error. The purpose of this research is to obtain a GWRK model between the factors that affect stunting density for each site viewed from the district center point in East Java Province and to make a prediction map based on the GWRK modeling. The data used was obtained from Basic Health Research (RISKESDAS) and the East Java Health Profile Book for 2021. The units of observation in this study were 38 districts in East Java.. Based on the GWR modeling results, it was found that the GWR model error contained spatial autocorrelation so that GWR model can be formed. From the GWRK modeling using stunting prevalence data in East Java in 2021, it was found that the GWR model was better than the global regression. Through prediction and prediction mapping formed from the GWR-Kriging modeling, it could be seen that stunting in regencies in East Java was evenly distributed . The interpolation map showed that the stunting forecasting values using the Kriging GWR interpolation ranged from 27% to 46%.</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/7638</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss1pp0495-0504</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; 0495-0504</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/7638/5568</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Atiek Iriany, Wigbertus Ngabu, danang Arianto, Arditama Putra</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>