Analisis Komparatif C4.5, XGBoost, dan CatBoost Berbasis SMOTE untuk Prediksi Kelayakan Calon Karyawan
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Putri Suci Ramdani
*
Universitas Bumigora, Nusa Tenggara Barat,
Indonesia
- Galih Hendro Martono Universitas Bumigora, Nusa Tenggara Barat, Indonesia
- Moch. Syahrir Universitas Bumigora, Nusa Tenggara Barat, Indonesia (*) Corresponding Author
Abstract
Recruitment is an important stage in helping organizations identify candidates who meet job requirements. Manual assessment of a large number of candidates can be time-consuming and may lead to inconsistent decisions. This study aims to compare the performance of C4.5, XGBoost, and CatBoost in classifying candidate eligibility and to determine the model with the most consistent performance. The AI Fair Recruitment Dataset was used, consisting of 119,971 candidate records. The study followed the CRISP-DM framework, including Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data preparation included attribute selection, encoding, a stratified split of 90% training data and 10% test data, and class imbalance handling using SMOTE applied only to the training data. The models were compared using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix. The results show that XGBoost and CatBoost achieved 98% accuracy, while C4.5 achieved 95%. CatBoost achieved 96% precision, 98% recall, 98% F1-score, and 97% ROC-AUC, providing the most consistent performance among the three models. The confusion matrix also shows that CatBoost produced the lowest classification errors, with 121 False Positive and 50 False Negative cases. These results indicate that CatBoost was the most suitable model under the experimental setting of this study. The model was subsequently implemented in a Flask-based prototype as an initial screening support tool. This study did not quantitatively evaluate algorithmic fairness; therefore, high predictive performance should not be interpreted as evidence that the model is bias-free.
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