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September 28, 2025Jurnal Teknik Informatika (Jutif)Open Access

Performance Optimization of Support Vector Machine with SMOTE for Multiclass Stunting Prediction in Sumedang District, Indonesia

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Authors

IFIrfan FadilRMRamdani Surya ManggalaEFEsa Firmansyah

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Overview

Performance evaluation highlights the effectiveness of SMOTE in improving SVM accuracy for predicting stunting.

Key Points

  • The application of SMOTE increased the SVM model’s accuracy from 85.10% to 89.08% in predicting stunting.
  • The F1-score improved for most classes, demonstrating the model's enhanced predictive capability after using SMOTE.
  • Utilizing RapidMiner software enabled efficient testing and evaluation of the SVM algorithm on an imbalanced dataset.
  • Combining SVM with SMOTE provides a reliable approach for tackling health-related multiclass predictions in public health.

Cite This Study

Fadil et al. (2025) studied this question.

synapsesocial.com/papers/68d90bc641e1c178a14f6eedhttps://doi.org/10.52436/1.jutif.2025.6.4.4843
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Also Consider

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

  1. 1Performance Comparison of Random Forest, SVM, and XGBoost Algorithms with SMOTE for Stunting Prediction2025 · 7 citations
  2. 2Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Classification, Prediction, and Clustering Optimization in Aceh, Indonesia2024 · 1 citations
  3. 3IMPROVING STUNTING CLASSIFICATION PERFORMANCE USING COMBINATION SMOTE TECHNIQUE AND ARTIFICIAL NEURAL NETWORK ALGORITHM2024 · 3 citations
  4. 4Stunting Classification Analysis for Toddlers in Bojongsoang: A Data-Driven Approach2024 · 9 citations
  5. 5Implementation Of Naïve Bayes Classifier And Support Vector Machine For Stunting Classification2024