Open access Jurnal SINTA 3
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
- ISSN
- 2615-3017, 1978-7227
- Published
- 2026-01-26
- Publisher
- PATTIMURA UNIVERSITY
- Language
- eng
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-author | Organisation | Joint articles |
|---|---|---|
| Evy Sulistianingsih | Affiliation unknown | 9 |
| Ray Tamtama | Affiliation unknown | 8 |
| Wirda Andani | Affiliation unknown | 4 |
| Siti Aprizkiyandari | Affiliation unknown | 4 |
| Dadan Kusnandar | Affiliation unknown | 2 |
| Hani Dwi Retnani | Affiliation unknown | 2 |
| Inggriani Millennia Taraly | Affiliation unknown | 2 |
| Steven Jansen Sinaga | Affiliation unknown | 2 |
| Widad Imtiyaz | Affiliation unknown | 2 |
| Dinda Tiara | Affiliation unknown | 2 |
| Fritzgerald Muhammad Arsyi | Affiliation unknown | 2 |
| Muhammad Fauzan | Affiliation unknown | 2 |
| Nabila Izza Harnanta | Affiliation unknown | 2 |
| Sania Pujianti | Affiliation unknown | 2 |
| Geby Marola | Affiliation unknown | 1 |
| Rita Apriliyanti | Affiliation unknown | 1 |
| Tiffany Ardhitha | Affiliation unknown | 1 |
| Indah Oktaviani | Affiliation unknown | 1 |
| Maria Welita Dwinanda | Affiliation unknown | 1 |
| Lisa Lestari | Affiliation unknown | 1 |
| Aditya Nugraha Pratama | Affiliation unknown | 1 |
| Naomi Nessyana Debataraja | Affiliation unknown | 1 |
| Nurfitri Imro'ah | Affiliation unknown | 1 |
| Herina Marlisa | Affiliation unknown | 1 |
| Rifkah Alfiyyah Sary | Affiliation unknown | 1 |
| Ainun Zakiah | Affiliation unknown | 1 |
| Noerul Hanin | Affiliation unknown | 1 |
| David Jordy Dhandio | Affiliation unknown | 1 |
| Hairil Al-Ham | Affiliation unknown | 1 |
| Preatin Preatin | Affiliation unknown | 1 |
| Louis Putra Jaya | Affiliation unknown | 1 |
| Hendri Kurniawan | Affiliation unknown | 1 |
| Yuyun Eka Pratiwi | Affiliation unknown | 1 |
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.