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MODELING STUNTING PREVALENCE IN INDONESIA USING SPLINE TRUNCATED SEMIPARAMETRIC REGRESSION

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 3 (2024) · pp. 2015–2028

DOI
10.30598/barekengvol18iss3pp2015-2028
ISSN
2615-3017, 1978-7227
Published
2024-07-31
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Semiparametric regression combines parametric and nonparametric regression approaches. It is employed when the relationship pattern of the response variable is known with some predictors, while for other predictors, the relationship pattern is uncertain. The parametric regression component in this study is linear regression, while the nonparametric component utilizes a spline truncated estimator, resulting in a semiparametric spline truncated regression model. The case study focuses on the prevalence of stunting across 34 provinces in Indonesia in 2022, revealing a relatively high prevalence of 21.60%. The research aims to determine the optimal number of knots, the best model, and factors influencing stunting prevalence in Indonesia. The findings indicate that the optimal three-knot model with a GCV of 9.30 yields an RMSE of 1.70 and R2 of 92.71%. Significance tests for simultaneous and partial parameters reveal that all predictor variables significantly influence stunting prevalence.

Collaboration network of the authors

36 authors · 76 co-authorship links

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Co-authorOrganisationJoint articles
Surya PranggaAffiliation unknown3
Zakiyah Mar'ahAffiliation unknown3
Meirinda FauziyahAffiliation unknown3
Ahmad Rizky KesumaAffiliation unknown2
Farikah Ayu RinandaAffiliation unknown2
Ilyas AstafiraAffiliation unknown2
Nur AfrianiAffiliation unknown2
Tri Septi Ayu LestariAffiliation unknown2
Anggi Putri AnisarAffiliation unknown2
Rahmania RahmaniaAffiliation unknown2
Eka Nur AmaliahAffiliation unknown1
Darnah DarnahAffiliation unknown1
Tirta PurnaragaAffiliation unknown1
Rifka Nurfaiza ZarkasiAffiliation unknown1
Aprianti Boma PadatuanAffiliation unknown1
Muhammad Danil PasarellaAffiliation unknown1
Fidia Deny Tisna AmijayaAffiliation unknown1
Memi Nor HayatiAffiliation unknown1
Yunda Sasha ParadillaAffiliation unknown1
Ar Ruum Mia SariAffiliation unknown1
Mohammad Nurul HudaAffiliation unknown1
Sitti AnggraeniAffiliation unknown1
Qonita Qurrota A'yunAffiliation unknown1
Frans Karta Sayoga SitohangAffiliation unknown1
Siti MahmudaAffiliation unknown1
Marisa Nanda SaputriAffiliation unknown1
Wasono WasonoAffiliation unknown1
Asnita AsnitaAffiliation unknown1
Indria SaskaAffiliation unknown1
Ghina Fadhilla MumtazAffiliation unknown1
Suyitno SuyitnoAffiliation unknown1
Ludia Ni'matuzzahrohAffiliation unknown1
Narita Yuri AdrianingsihAffiliation 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) 
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	<dc:title xml:lang="en-US">MODELING STUNTING PREVALENCE IN INDONESIA USING SPLINE TRUNCATED SEMIPARAMETRIC REGRESSION</dc:title>
	<dc:creator>Fadlirhohim, Rizki Dwi</dc:creator>
	<dc:creator>Sifriyani, Sifriyani</dc:creator>
	<dc:creator>Dani, Andrea Tri Rian</dc:creator>
	<dc:description xml:lang="en-US">Semiparametric regression combines parametric and nonparametric regression approaches. It is employed when the relationship pattern of the response variable is known with some predictors, while for other predictors, the relationship pattern is uncertain. The parametric regression component in this study is linear regression, while the nonparametric component utilizes a spline truncated estimator, resulting in a semiparametric spline truncated regression model. The case study focuses on the prevalence of stunting across 34 provinces in Indonesia in 2022, revealing a relatively high prevalence of 21.60%. The research aims to determine the optimal number of knots, the best model, and factors influencing stunting prevalence in Indonesia. The findings indicate that the optimal three-knot model with a GCV of 9.30 yields an RMSE of 1.70 and R2 of 92.71%. Significance tests for simultaneous and partial parameters reveal that all predictor variables significantly influence stunting prevalence. </dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-07-31</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/12964</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss3pp2015-2028</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application; 2015-2028</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss3year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12964/8550</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Rizki Dwi Fadlirhohim, Sifriyani Sifriyani, Andrea Tri Rian Dani</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>