<![CDATA[Stunting is a chronic nutritional problem among infants and toddlers that affects children’s growth and development. Various studies have utilized machine learning for nutritional status classification based on anthropometric data; however, the validity of the resulting models has rarely been examined. This study aims to evaluate the validity of machine learning models in classifying stunting status using the XGBoost, Random Forest, and Naïve Bayes algorithms. The dataset consists of 120,999 anthropometric records of infants, with age, gender, and height as features, and nutritional status as the target variable. The research process included preprocessing, data transformation, and model evaluation using the k-fold cross-validation method with accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest and XGBoost achieved very high accuracy, at 99.91% and 99.08%, respectively, while Naïve Bayes reached only 55%. This stark difference in performance indicates that ensemble-based models are capable of capturing very strong patterns in the data, while Naïve Bayes struggles due to the interdependence among features. Furthermore, the high accuracy of certain models suggests a deterministic relationship between features and labels, which could potentially make the models less robust against data containing measurement errors or noise.]]>
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