Identification of Factors that Influence Stunting Cases in South Sulawesi using Geographically Weighted Regression Modeling


  • Siswanto Siswanto Hasanuddin University
  • Mirna Mirna Universitas Hasanuddin
  • Muhammad Yusran Universitas Hasanuddin
  • Ummul Auliyah Syam Universitas Hasanuddin
  • Alya Safira Irtiqa Miolo Universitas Hasanuddin



children, geographically weighted regression, stunting


In Indonesia, nearly seven million children under five are stunted and throughout the world, Indonesia is the country with the fifth-highest stunting prevalence. South Sulawesi ranks fourth with a high stunting potential in Indonesia. Stunting is caused by multi-dimensional factors and not only due to malnutrition experienced by pregnant women and children under five. In more detail, several factors that cause stunting are the effects of poor care, the lack of household/family access to nutritious food, and the lack of access to clean water and sanitation. In addition to maternal characteristics and parenting, the problem of stunting is also influenced by environmental factors and geographical conditions (population density, climatic conditions, and inadequate sanitation) so the spatial analysis is important to do in overcoming this problem. In spatial data, often observations at a location (space) depend on observations at other locations that are nearby (neighboring). By using Geographically Weighted Regression (GWR) obtained variables that affect the prevalence of stunting in South Sulawesi Province, including the percentage of babies receiving vitamin A intake, the percentage of babies receiving exclusive breastfeeding, the percentage of babies receiving health care, the percentage of malnourished children under five, the percentage short toddlers, the percentage of infants receiving DPT-HB-Hib, Measles and BCG immunizations.  for the GWR model is 81.32% and based on variables that are significant to the prevalence of stunting in South Sulawesi Province, three clusters are formed.


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How to Cite

Siswanto, S., Mirna, M., Yusran, M. ., Syam, U. A. ., & Miolo, A. S. I. . (2022). Identification of Factors that Influence Stunting Cases in South Sulawesi using Geographically Weighted Regression Modeling. Jurnal Matematika, Statistika Dan Komputasi, 19(1), 100-108.



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