Classification of Stunting in Children Using the C4.5 Algorithm

Autor: Muhajir Yunus, Muhammad Kunta Biddinika, Abdul Fadlil
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: JOIN: Jurnal Online Informatika, Vol 8, Iss 1, Pp 99-106 (2023)
Druh dokumentu: article
ISSN: 2528-1682
2527-9165
DOI: 10.15575/join.v8i1.1062
Popis: Stunting is a disease caused by malnutrition in children, which results in slow growth. Generally, stunting is characterized by a lack of weight and height in young children. This study aims to classify stunting in children aged 0-60 months using the Decision Tree C4.5 method based on z-score calculations with a sample size of 224 records, consisting of 4 attributes and 1 label, namely Gender, Age, Weight, Height, and Nutritional Status. The results of the study obtained a C4.5 decision tree where the Age variable influenced the classification of stunting with the highest Gain Ratio of 0.185016337. Meanwhile, the evaluation of the model using the Confusion matrix resulted in the highest accuracy of 61.82% and AUC of 0.584.
Databáze: Directory of Open Access Journals