Supervised models to predict the Stunting in East Aceh

Nowadays, Undernutrition is the main cause of child death in developing countries. There are many people and organizations try to mitigate or minimize case death. Thus, this paper aimed has excellent method handle undernutrition by exploring efficacy machine learning (ML) approaches predict Stunting East Aceh administrative zones Indonesia identify most important predictors. The study employed ML techniques using retrospective cross-sectional survey data from Aceh, a national-representative collected government 2019 about stunting data. We explored Random forest commonly used algorithms. Forest (RF) as an extension bagging that addition for taking random sample also uses subset features which mitigates over fitting. Our results showed considered classification algorithms can effectively status zones. Persistent was found east part Aceh. identification high-risk provide more useful information decision-makers trying reduce undernutrition.