DOI: https://doi.org/10.14710/j.gauss.15.1.12-23

MODELING CHILD MALNUTRITION IN EAST JAVA USING THE INTEGRATION OF PARTIAL LEAST SQUARE WITH IMPORTANCE-PERFORMANCE ANALYSIS

*Zulfani Alfasanah  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia
Bambang Widjanarko Otok  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia
Muhammad Ahsan  -  Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Child malnutrition remains a persistent public health concern in Indonesia, particularly in East Java, where stunting and undernutrition rates remain high despite national progress. Malnutrition in children is a multidimensional issue, influenced not only by dietary intake and disease but also by socio-economic conditions, caregiving practices, and environmental health factors such as access to clean water, sanitation, and health services. Addressing this complexity requires robust analytical methods. This study applies Partial Least Squares–Importance Performance Analysis (PLS-IPA) to model the pathways influencing child malnutrition and identify priority interventions. Results indicate that socio-economic factors act as primary predictors influencing food security, healthcare access, and parenting practices. Among direct determinants, environmental health services, including sanitation, clean water access, and maternal healthcare, show the strongest direct effect in reducing malnutrition. IPA results highlight food security as the most critical intervention priority due to its high importance but moderate field performance. Strengthening socio-economic development, improving food security, and sustaining environmental health interventions are recommended to support long-term reductions in child malnutrition in East Java.
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Keywords: Child malnutrition; Importance-Performance Analysis; Partial Least Square; Structural Equation Modeling

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