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October 9, 2025Khazanah Informatika Jurnal Ilmu Komputer dan InformatikaOpen Access

Performance Comparison of Random Forest, Bagging, and CART Methods in Classifying Recipients of the Family Program in North Aceh

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Authors

MYMeri Hari YanniKNKhairil Anwar NotodiputroBSBagus Sartono

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Overview

Performance measures of random forest and bagging methods exceed cart in classifying North Aceh families.

Key Points

  • Random Forest achieved an accuracy of 90% using the SMOTE technique for unbalanced classes in the classification task.
  • The Bagging method also performed well, with an accuracy of 86%, showing improved stability and reduced variance.
  • Evaluation metrics included accuracy, sensitivity, specificity, precision, F1 score, and AUC, demonstrating Robust performance.
  • The CART method underperformed with low accuracy, indicating limitations in accurately predicting family recipients under this model.

Cite This Study

Yanni et al. (2025) studied this question.

synapsesocial.com/papers/68e7d631bd66d359be62686dhttps://doi.org/10.23917/khif.v11i1.5098
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