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SEGMENTING MATERNAL AND CHILD HEALTH DEGREE IN INDONESIA USING SEM-PLS POS

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 20 No. 2 (2026) · pp. 0897–0912

DOI
10.30598/barekengvol20iss2pp0897-0912
ISSN
2615-3017, 1978-7227
Published
2026-01-26
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Maternal and child health represents a critical aspect of national development, reflecting both the well-being of the population and the success of regional health equity programs. In Indonesia, disparities in maternal and child health outcomes remain evident across provinces due to socio-economic inequalities and unequal access to health services. This study aims to model the causal relationships between key health determinants and classify provinces based on maternal and child health degree using Structural Equation Modeling with Partial Least Squares (SEM-PLS) combined with Prediction-Oriented Segmentation (POS). The study uses secondary data from 2023 sourced from the Central Statistics Agency (BPS) and the Ministry of Health, covering 34 provinces at the provincial level. Nineteen indicators are grouped into four latent variables: Health Services, Clean and Healthy Living Behavior (PHBS), Environment, and Health Degree. SEM-PLS was applied to identify direct and indirect relationships among these variables, while POS was used to identify homogeneous segments of provinces. The results show that PHBS positively affects Environment (path coefficient = 0.896; p < 0.001), while Health Services negatively affect Health Degree (path coefficient = –0.668; p < 0.01), indicating the presence of indirect pathways influencing health outcomes. The segmentation analysis identified three segments: Segment 1 includes provinces with moderate outcomes but weak child health services; Segment 2 includes provinces with relatively better maternal outcomes but sanitation gaps; Segment 3 consists of provinces with the most critical health conditions, including high stunting and malnutrition rates. These findings demonstrate that PHBS is a dominant influencing factor, while improved service access alone does not always translate to better outcomes. The SEM-POS approach effectively identifies segment-specific health disparities, supporting more targeted policy interventions to improve maternal and child health in Indonesia.

Collaboration network of the authors

36 authors · 78 co-authorship links

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Co-authorOrganisationJoint articles
Evy SulistianingsihAffiliation unknown9
Ray TamtamaAffiliation unknown8
Wirda AndaniAffiliation unknown4
Siti AprizkiyandariAffiliation unknown4
Dadan KusnandarAffiliation unknown2
Hani Dwi RetnaniAffiliation unknown2
Inggriani Millennia TaralyAffiliation unknown2
Steven Jansen SinagaAffiliation unknown2
Widad ImtiyazAffiliation unknown2
Dinda TiaraAffiliation unknown2
Fritzgerald Muhammad ArsyiAffiliation unknown2
Muhammad FauzanAffiliation unknown2
Nabila Izza HarnantaAffiliation unknown2
Sania PujiantiAffiliation unknown2
Geby MarolaAffiliation unknown1
Rita ApriliyantiAffiliation unknown1
Tiffany ArdhithaAffiliation unknown1
Indah OktavianiAffiliation unknown1
Maria Welita DwinandaAffiliation unknown1
Lisa LestariAffiliation unknown1
Aditya Nugraha PratamaAffiliation unknown1
Naomi Nessyana DebatarajaAffiliation unknown1
Nurfitri Imro'ahAffiliation unknown1
Herina MarlisaAffiliation unknown1
Rifkah Alfiyyah SaryAffiliation unknown1
Ainun ZakiahAffiliation unknown1
Noerul HaninAffiliation unknown1
David Jordy DhandioAffiliation unknown1
Hairil Al-HamAffiliation unknown1
Preatin PreatinAffiliation unknown1
Louis Putra JayaAffiliation unknown1
Hendri KurniawanAffiliation unknown1
Yuyun Eka PratiwiAffiliation 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">SEGMENTING MATERNAL AND CHILD HEALTH DEGREE IN INDONESIA USING SEM-PLS POS</dc:title>
	<dc:creator>Nurhanifa, Nurhanifa</dc:creator>
	<dc:creator>Perdana, Hendra</dc:creator>
	<dc:creator>Satyahadewi, Neva</dc:creator>
	<dc:description xml:lang="en-US">Maternal and child health represents a critical aspect of national development, reflecting both the well-being of the population and the success of regional health equity programs. In Indonesia, disparities in maternal and child health outcomes remain evident across provinces due to socio-economic inequalities and unequal access to health services. This study aims to model the causal relationships between key health determinants and classify provinces based on maternal and child health degree using Structural Equation Modeling with Partial Least Squares (SEM-PLS) combined with Prediction-Oriented Segmentation (POS). The study uses secondary data from 2023 sourced from the Central Statistics Agency (BPS) and the Ministry of Health, covering 34 provinces at the provincial level. Nineteen indicators are grouped into four latent variables: Health Services, Clean and Healthy Living Behavior (PHBS), Environment, and Health Degree. SEM-PLS was applied to identify direct and indirect relationships among these variables, while POS was used to identify homogeneous segments of provinces. The results show that PHBS positively affects Environment (path coefficient = 0.896; p &amp;lt; 0.001), while Health Services negatively affect Health Degree (path coefficient = –0.668; p &amp;lt; 0.01), indicating the presence of indirect pathways influencing health outcomes. The segmentation analysis identified three segments: Segment 1 includes provinces with moderate outcomes but weak child health services; Segment 2 includes provinces with relatively better maternal outcomes but sanitation gaps; Segment 3 consists of provinces with the most critical health conditions, including high stunting and malnutrition rates. These findings demonstrate that PHBS is a dominant influencing factor, while improved service access alone does not always translate to better outcomes. The SEM-POS approach effectively identifies segment-specific health disparities, supporting more targeted policy interventions to improve maternal and child health in Indonesia.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2026-01-26</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/17803</dc:identifier>
	<dc:identifier>10.30598/barekengvol20iss2pp0897-0912</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application; 0897-0912</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol20iss2year2026</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17803/12428</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Nurhanifa Nurhanifa, Hendra Perdana, Neva Satyahadewi</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>