Comparison of C4.5 Decision Tree and Naive Bayes Algorithms for Classification of Nutritional Status in Stunting Toddlers

Abstract

Stunting is a condition where growth and development of children under 5 years of age is impaired due to chronic malnutrition. Data mining with classification techniques on the nutritional status of stunting toddlers can be performed to help identify toddlers experiencing stunting and provide objective measurements of their nutritional status. There are several classification methods, but this research will compare the performance of the C4.5 decision tree algorithm, which is included in the decision tree approach, and naive Bayes, which uses a probability-based approach of class occurrence in classifying nutritional status of stunting toddlers, with discretization performed in the preprocessing stage. The data used in this research was obtained from Jagir Health Center, Surabaya, in the form of secondary data on toddler nutrition in 2021, totaling 2,801 records. The labeling of stunting or normal in the dataset uses the reference of child anthropometric standards in Indonesia as stated in the Republic of Indonesia Minister of Health Regulation number 2 of 2020. The best method based on the AUC (Area Under the Curve) value was the C4.5 decision tree with a value of 86% (good classification), while naive Bayes achieved 77% (fair classification) using a 70:30 training and testing data ratio.