International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 8 (August 2025), Pages: 273-286

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 Original Research Paper

Predicting stunting incidence in Pidie, Indonesia using SEM-PLS and GIS: A multilevel analysis of determinants

 Author(s): 

 Putri Ilham Sari 1, Herlina Dimiati 2, *, Sofia Sofia 1, Muhammad Subianto 3

 Affiliation(s):

  1Doctoral Program in Medical Science, Faculty of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia
  2Department of Pediatrics, Faculty of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia
  3Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0002-3553-2301

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.08.025

 Abstract

Stunting is a complex child health issue caused by poor nutrition, infections, and environmental factors. The Pidie District in Aceh, Indonesia, has been identified as a high-priority area for stunting intervention, with five sub-districts showing high prevalence rates. This study aims to identify the key determinants of stunting at the individual, household, and community levels and to develop a predictive model for stunting incidence in these areas. Structural Equation Modeling-Partial Least Squares (SEM-PLS) and Geographic Information Systems (GIS) were used to analyze the causal relationships and spatial distribution of stunting. Data were collected using a structured questionnaire and total sampling method, involving 988 mother-child pairs across Tangse (211), Tiro (46), Kembang Tanjong (321), Simpang Tiga (361), and Mutiara Timur (49). SEM-PLS results indicated that stunting is consistent with the Conceptual Childhood Stunting Model and the Social Determinants of Health (CSDH) framework, with the model explaining 73.2% of the variance in stunting cases (R² = 0.732). GIS mapping showed that Kembang Tanjong had the highest number of cases (184), while Tiro had the lowest (25). The main contributing factors were individual characteristics, household socioeconomic status, and community conditions. These findings suggest that targeted interventions addressing local risk factors, along with improvements in nutrition, healthcare access, and socioeconomic development, are essential to reduce stunting in the Pidie District.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Child stunting, Determinant factors, SEM-PLS, GIS mapping, Pidie District

 Article history

 Received 17 February 2025, Received in revised form 13 July 2025, Accepted 4 August 2025

 Acknowledgment

The authors wish to express their profound gratitude to the sub-district heads, village leaders, health center personnel, and the communities within the five sub-districts of Tangse, Tiro, Kembang Tanjong, Simpang Tiga, and Mutiara Timur for their invaluable contributions to this study. Furthermore, we sincerely appreciate the supervisors, examiners, and promoters’ guidance and constructive feedback, which have substantially enhanced this research's quality and scientific rigor. 

 Compliance with ethical standards

 Ethical considerations

This study received approval from the Research Ethics Committee of Syiah Kuala University, Darussalam, Banda Aceh, Indonesia, under number 109/EA/FK/2023. Moreover, before data collection, enumerators underwent training in stunting assessment methodologies conducted by faculty members from Syiah Kuala University to guarantee the accuracy and reliability of data collection. Data collection was organized based on the availability of respondents, aligning with prior agreements established between participants and enumerators. This methodology ensured the data's validity and upheld ethical standards concerning respondent engagement.

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Sari PI, Dimiati H, Sofia S, and Subianto M (2025). Predicting stunting incidence in Pidie, Indonesia using SEM-PLS and GIS: A multilevel analysis of determinants. International Journal of Advanced and Applied Sciences, 12(8): 273-286

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 Figures

  Fig. 1   Fig. 2   Fig. 3   Fig. 4   Fig. 5   Fig. 6   Fig. 7  

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5  Table 6  Table 7  Table 8  Table 9  Table 10

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