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September 10, 2025

Comparison of Machine Learning Algorithms for Stunting Classification

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Authors

MYMuhajir YunusUniversitas Muhammadiyah GorontaloMBMuhammad Kunta BiddinikaUniversitas Ahmad DahlanAFAbdul FadlilUniversitas Ahmad Dahlan

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Implication

Analysis demonstrates that decision tree C4.5 achieves 87% accuracy in stunting classification, suggesting improved outcomes for children at risk.

Key Points

  • Decision tree C4.5 algorithm achieved the highest accuracy of 87% in stunting classification.
  • Naive Bayes algorithm yielded a lower accuracy of 71% in classifying stunting events in children.
  • Machine learning techniques were evaluated using a dataset of child health information consisting of 224 records.
  • Findings indicate that the decision tree C4.5 may be a valuable tool for improving stunting classification efforts.

Cite This Study

Yunus et al. (2025) studied this question.

synapsesocial.com/papers/68c1afd354b1d3bfb60e820ahttps://doi.org/10.64539/sjer.v1i2.2025.9
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia2024 · 4 citations
  2. 2Implementation Of Naïve Bayes Classifier And Support Vector Machine For Stunting Classification2024
  3. 3Implementation of Naïve Bayes and K-NN Algorithms in Diagnosing Stunting in Children2024 · 3 citations
  4. 4Stunting Classification Analysis for Toddlers in Bojongsoang: A Data-Driven Approach2024 · 9 citations
  5. 5Comparative Analysis of Machine Learning Algorithm Performance in Predicting Stunting in Toddlers2024