Modeling the Cart Method on the Factors that Cause Stunting

Modeling the Classification and Regression Tree (CART) method to identify and address the factors causing stunting can significantly aid in reducing stunting rates. Stunting, a chronic malnutrition problem, is influenced by various factors, including poor nutrition, repeated infections, inadequate psychosocial stimulation, and low socioeconomic status. Key determinants such as maternal education, exclusive breastfeeding, low birth weight, and environmental sanitation play crucial roles in stunting prevalence. Effective interventions include improving antenatal care, contraceptive use, and handwashing practices, which have been shown to reduce stunting rates by significant margins. Community empowerment through education and socialization, mainly targeting mothers and young women, is essential for stunting prevention and management. The CART method can effectively model these multifaceted factors by identifying the most significant predictors and their interactions, guiding targeted interventions to reduce stunting rates. By leveraging comprehensive data and community-based strategies, the CART method can provide a robust framework for reducing stunting and improving child health outcomes. The findings of this study are based on data variables, including gender, exclusive breastfeeding, height, and age of toddlers, which are determinants of the accuracy of stunting cases. Analysis of the CART method shows results that can be used in decision-making related to stunting data. In 200 cases, 75.5% accuracy was obtained with this method. The results of this study provide an overview so that future research can use other, more accurate methods and add more variables to the cause of stunting.