Classification of Stunting in Children Using the C4.5 Algorithm

Authors

  • Muhajir Yunus Master Program of Informatics, Universitas Ahmad Dahlan, Indonesia , Indonesia
  • Muhammad Kunta Biddinika Master Program of Informatics, Universitas Ahmad Dahlan, Indonesia , Indonesia
  • Abdul Fadlil Electrical Engineering, Universitas Ahmad Dahlan, Indonesia, Indonesia

DOI:

https://doi.org/10.15575/join.v8i1.1062

Keywords:

C4.5 Algorithm, Classification, Machine Learning, Stunting

Abstract

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.

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Published

2023-06-28

Issue

Vol. 8 No. 1 (2023)

Section

Article

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License