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Identification of Factors that Influence Stunting Cases in South Sulawesi using Geographically Weighted Regression Modeling

Jurnal Matematika, Statistika dan Komputasi · Vol. 19 No. 1 (2022) · pp. 100–108

DOI
10.20956/j.v19i1.21617
ISSN
2614-8811, 1858-1382
Published
2022-09-07
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

In Indonesia, nearly seven million children under five are stunted and throughout the world, Indonesia is the country with the fifth-highest stunting prevalence. South Sulawesi ranks fourth with a high stunting potential in Indonesia. Stunting is caused by multi-dimensional factors and not only due to malnutrition experienced by pregnant women and children under five. In more detail, several factors that cause stunting are the effects of poor care, the lack of household/family access to nutritious food, and the lack of access to clean water and sanitation. In addition to maternal characteristics and parenting, the problem of stunting is also influenced by environmental factors and geographical conditions (population density, climatic conditions, and inadequate sanitation) so the spatial analysis is important to do in overcoming this problem. In spatial data, often observations at a location (space) depend on observations at other locations that are nearby (neighboring). By using Geographically Weighted Regression (GWR) obtained variables that affect the prevalence of stunting in South Sulawesi Province, including the percentage of babies receiving vitamin A intake, the percentage of babies receiving exclusive breastfeeding, the percentage of babies receiving health care, the percentage of malnourished children under five, the percentage short toddlers, the percentage of infants receiving DPT-HB-Hib, Measles and BCG immunizations. for the GWR model is 81.32% and based on variables that are significant to the prevalence of stunting in South Sulawesi Province, three clusters are formed.

Keywords

Collaboration network of the authors

40 authors · 122 co-authorship links · showing the 40 most connected

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  • Padjadjaran University
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
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Co-authorOrganisationJoint articles
Anisa KalondengAffiliation unknown4
Sri Astuti ThamrinAffiliation unknown3
Sitti SahrimanAffiliation unknown2
Andi Isna YunitaAffiliation unknown2
Andi M Alfin BasoAffiliation unknown2
M. Zaky Hisyam GozhiAffiliation unknown2
Vika Yugi KurniawanAffiliation unknown2
Nurtiti SunusiAffiliation unknown2
Ade Safira SeptianyAffiliation unknown2
Raupong RaupongAffiliation unknown2
Budi AyuningsihPadjadjaran University1
Iman HernamanPadjadjaran University1
Tidi DhalikaPadjadjaran University1
Destu Putra RosmawanAffiliation unknown1
Dedi RahmatPadjadjaran University1
Devi Savitri EffendyUniversitas Halu Oleo1
Ramadhan TosepuUniversitas Halu Oleo1
Sri SusantyAffiliation unknown1
Nurna NingsihAffiliation unknown1
Dhidharmadi HassanudinAffiliation unknown1
Nuraisya NuraisyaAffiliation unknown1
Ansharullah AnsharullahUniversitas Halu Oleo1
Abdu Rahman BacoAffiliation unknown1
Alfiana AntohAffiliation unknown1
Yundy HafizriandaAffiliation unknown1
Anna IslamiyatiAffiliation unknown1
Hajratul Ashwad KAffiliation unknown1
Afifah Mutiah NurdinAffiliation unknown1
Muh. Indirwan ArfanAffiliation unknown1
Muhammad FadilAffiliation unknown1
A.Muthiah Nur AngrianyAffiliation unknown1
Rini JamalAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1
Haksar HaksarAffiliation 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/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Identification of Factors that Influence Stunting Cases in South Sulawesi using Geographically Weighted Regression Modeling</dc:title>
	<dc:creator xml:lang="en">Siswanto, Siswanto</dc:creator>
	<dc:creator xml:lang="en">Mirna, Mirna</dc:creator>
	<dc:creator xml:lang="en">Yusran, Muhammad </dc:creator>
	<dc:creator xml:lang="en">Syam, Ummul Auliyah </dc:creator>
	<dc:creator xml:lang="en">Miolo, Alya Safira Irtiqa </dc:creator>
	<dc:subject xml:lang="en">children</dc:subject>
	<dc:subject xml:lang="en">geographically weighted regression</dc:subject>
	<dc:subject xml:lang="en">stunting</dc:subject>
	<dc:description xml:lang="en">In Indonesia, nearly seven million children under five are stunted and throughout the world, Indonesia is the country with the fifth-highest stunting prevalence. South Sulawesi ranks fourth with a high stunting potential in Indonesia. Stunting is caused by multi-dimensional factors and not only due to malnutrition experienced by pregnant women and children under five. In more detail, several factors that cause stunting are the effects of poor care, the lack of household/family access to nutritious food, and the lack of access to clean water and sanitation. In addition to maternal characteristics and parenting, the problem of stunting is also influenced by environmental factors and geographical conditions (population density, climatic conditions, and inadequate sanitation) so the spatial analysis is important to do in overcoming this problem. In spatial data, often observations at a location (space) depend on observations at other locations that are nearby (neighboring). By using Geographically Weighted Regression (GWR) obtained variables that affect the prevalence of stunting in South Sulawesi Province, including the percentage of babies receiving vitamin A intake, the percentage of babies receiving exclusive breastfeeding, the percentage of babies receiving health care, the percentage of malnourished children under five, the percentage short toddlers, the percentage of infants receiving DPT-HB-Hib, Measles and BCG immunizations.  for the GWR model is 81.32% and based on variables that are significant to the prevalence of stunting in South Sulawesi Province, three clusters are formed.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2022-09-07</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/jmsk/article/view/21617</dc:identifier>
	<dc:identifier>10.20956/j.v19i1.21617</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 19 No. 1 (2022): SEPTEMBER, 2022; 100-108</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/21617/8613</dc:relation>
	<dc:relation>10.20956/j.v19i1.21617.g8613</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>