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September 27, 2025Dinasti International Journal of Education Management And Social ScienceOpen Access

Comparative Analysis of Gradient Boosting, XGBoost, and KNN on Predicting Student Graduation in Imbalance and Balance Data Schemes

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Authors

MHMuhammad Rizki HubuIPIrfan Pratama

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Overview

Analysis compares gradient boosting, xgboost, and KNN on imbalanced data, highlighting performance improvements.

Key Points

  • XGBoost achieved perfect scores of accuracy, precision, recall, and F1 after applying balancing techniques.
  • Gradient Boosting maintained high performance with a score of 0.9992 consistently during the analysis.
  • KNN improved accuracy significantly from 0.9928 to 0.9968 post-balancing, demonstrating its effectiveness.
  • The SMOTE-TOMEK technique was found effective for enhancing classification model performance on imbalanced data.

Cite This Study

Hubu et al. (2025) studied this question.

synapsesocial.com/papers/68d7be6ceebfec0fc52380e9https://doi.org/10.38035/dijemss.v6i6.5362
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