Comparison Of Machine Learning Algorithms On Stunting Detection For 'Centing' Mobile Application To Prevent Stunting

Stunting is a growth disorder caused by chronic undernutrition, with long-term impacts on child health and development. In Indonesia, the prevalence of stunting reached 31.8% in children under five years old in 2018, indicating an urgent need for effective interventions. In an effort to address this issue, we developed a mobile application called Centing (Cegah Stunting) that utilizes machine learning for early detection and prevention of stunting. In this study, we compare the performance of four machine learning algorithms Logistic Regression, Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP) in detecting children's nutritional status based on a dataset from Kaggle with 121 thousand data and four main features: age, gender, height, and nutritional status. The experimental results show that SVM with RBF kernel and CNN achieved the highest accuracy of 98%, while Logistic Regression and MLP achieved 76% and 97% accuracy respectively. SVM with RBF kernel was chosen as the best model due to its high accuracy and efficiency in computation time. These findings suggest that the Centing application, with the implementation of SVM RBF, has significant potential in early detection and prevention of stunting, and makes an important contribution to improving child health in Indonesia.