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

Abstract

Indonesia continues to encounter numerous challenges, particularly in the health and economic sectors. As the future of the nation, the quality of human resources is crucial for Indonesia's development. The development of Indonesia is key to improving the quality of life of its people, and a focus on this development can positively impact the health and economy of the community. A healthy and educated generation is fundamental for the country's expected progress, as nutritional status is one of the factors significantly affecting the quality of human resources. Nutritional problems can cause serious impacts, such as improper physical growth, decreased IQ quality, and even death. The goal is to analyze the factors affecting the nutritional status of toddlers by classifying each variable using a decision tree. A decision tree is a flow chart that resembles a branching tree structure. The C4.5 algorithm was utilized in this study. It can process both numeric and categorical data, handle missing attribute values, and generate easy-to-interpret rules. After conducting the analysis, it was found that there are 392 districts/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 and achieved an accuracy of 99.8% and a kappa value close to 1. This indicates that the model can accurately classify toddler nutrition problems in Indonesia.