Stunting Disease Classification Using Multi-Layer Perceptron Algorithm with GridSearchCV

Abstract

Stunting is a growth and development disorder caused by malnutrition characterized by a child's height less than the standard deviation set by WHO. In 2022, stunting cases in Indonesia are considered a high prevalence rate, reaching 21.6%. There are several factors that can cause stunting in children, namely maternal and antenatal care factors, home environment factors, breastfeeding practices, and feeding factors during toddlerhood. There are several impacts that occur when children are stunted, namely increased risk of child mortality, susceptibility to illness, impaired brain development, physical disorders and metabolic disorders. Currently, deep learning has been widely used for disease classification and prediction, one of the deep learning methods is Multi-Layer Perceptron (MLP). The purpose of this research is to classify stunting disease using a deep learning method, namely MLP. The dataset used consists of 8 attributes, namely gender, age, birth weight, birth length, body weight, body length, breastfeeding and stunting with a total of 10,000 records. The encoding process is carried out to convert categorical data into numeric attributes of gender, breastfeeding, and stunting. This research produces a higher accuracy value than previous research which used the C4.5 algorithm with an accuracy of 61.82%, whereas in this study using MLP which was integrated with the GridSearchCV hyperparameter it obtained an accuracy of 82.37%. This proves that the MLP method is successful in classifying stunting compared to previous research algorithms.