<![CDATA[Determining the right leader is a crucial factor in organizational success, including in the banking sector. This study aims to compare the performance of two popular classification algorithms, namely Naïve Bayes and C4.5, in the selection process of Branch Sub-Leaders at PT. Bank XYZ. Using a data mining approach, the research analyzes historical employee data encompassing personal attributes, competencies, and strategic priorities. The evaluation was conducted using a confusion matrix and ROC curve to measure accuracy, precision, recall, and F1-score for each algorithm. The experimental results show that C4.5 delivers superior performance, achieving an accuracy of 0.985 and an AUC of 1.000 in the binary scenario, while Naïve Bayes only reached an accuracy of 0.296 and an AUC of 0.8365. This study confirms that C4.5 is recommended as the primary model to support decision-making by providing the most suitable classification method for objective and transparent leadership placement. Furthermore, it contributes to sustainable managerial strategies through high accuracy and strong interpretability]]>
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