Classification of Nutrition Problems for Indonesian Toddler With Decision Tree Algorithm C4.5

Abstract

Having excellent human resources is essential for Indonesia's development. The development of Indonesia is the key to improving the quality of life for its citizens, and a focus on this development can have a positive impact on the health and economy of the community. A healthy and educated generation is fundamental for the expected progress of this nation, as nutritional status is a significant factor affecting the quality of human resources. Nutritional problems can lead to serious consequences, such as abnormal physical growth, a decline in IQ quality, and even death. The objective of this research is to analyze the factors that influence the nutritional status of toddlers by classifying each variable using a decision tree. A decision tree is a flowchart resembling a branching tree structure. The C4.5 algorithm was utilized in this study. This algorithm can process both numeric and categorical data, handle missing attribute values, and generate easily interpretable rules. After conducting the analysis, it was found that the decision tree's results indicated that the attribute "Stunting < 20%" is a determining factor for acutechronic malnutrition issues in toddlers. There are 392 districts and cities in Indonesia where the prevalence of stunted toddler nutritional status is less than 20%. The model created using the C4.5 algorithm was evaluated using a confusion matrix, resulting in an accuracy of 99.8% and a kappa value close to 1. This indicates that the model is capable of accurately classifying toddler nutrition problems in Indonesia.