Analysis of Multidimensional Stunting Intervention Factor Using Mixed Model

Abstract The mixed model combines fixed effect for all groups and random representing the diversity inter in (province) to increase precision. This study provides information on significance of multidimensional stunting intervention factors (predictor variables) prevalence (response variables as indicator 2.2.1 Sustainable Development Goals/SDGs) with district/city observation units. Using official data from Statistics Indonesia (National Socio Economic Survey) Ministry Health (Basic Research), this expects be one basis government, stakeholders, further research accelerate Indonesia’s SDGs targets 2030. Comparison classical linear method Least Absolute Shrinkage Selection Operator (Lasso) variable selection conduct relatively better results modelling Lasso. showed that predictor variables, namely complete immunization, ease access health facilities, food intake, improve water, expenditure per capita, children’s participation early childhood education, maternal ownership National Insurance toddlers, significantly affected decrease. low birth weight, households social protection cards, percentage poor people, prevalence.