Classification of Stunting Prevalence Levels in Indonesia
Keywords:
Dimension Reduction, Classification, Autoencoder, K-Nearest Neighbors, StuntingAbstract
Stunting is a problem of chronic malnutrition in children that affects their productivity in adults, cognitive development, and learning capacity. The high prevalence of stunting in Indonesia is caused by various health, social, and environmental factors that are different in each region, making it necessary to classify regions to identify areas with a high risk of stunting. Data with many variables present high-dimensionality issues that can reduce the accuracy of distance-based classification due to the curse of dimensionality. This study applies Autoencoder and K-Nearest Neighbors (KNN) on stunting data in Indonesia. The Autoencoder is used to reduce data dimensions through a process of reduction and reconstruction, thereby generating latent features for KNN input. The results show that the Autoencoder reduces 11 input variables to 4 latent features. KNN classification with K=7 categorizes provinces into 12 provinces with medium risk, 21 provinces with high risk, and 3 provinces with very high risk, with an accuracy of 75%. This study showed that dimensionality reduction using an Autoencoder improves data representation, enabling KNN to classify stunting more effectively
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