Application of Data Mining for Talent Performance Analysis Using The C.45 Method In A Case Study of The Human Resource Department of PT. Xyz
DOI:
https://doi.org/10.55606/jeei.v2i3.3710Keywords:
C4.5 Algorithm, Data Mining, Employee Performance, Human Resource Management, Talent PerformanceAbstract
Human resource management plays a critical role in supporting organizational performance, particularly in identifying employees with high competency and leadership potential. The process of evaluating employee performance is often conducted manually, which may lead to subjectivity and inconsistencies in decision-making. This study aims to implement the C4.5 decision tree algorithm for talent performance analysis within the Human Resource Department of PT. XYZ. The research utilized employee performance data collected during the 2022–2023 period, including variables such as attendance, achievement, assessment results, service period, and other competency-related indicators. The study adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. The C4.5 algorithm was employed to classify employee competencies and generate decision rules based on entropy and information gain calculations. The results indicate that the algorithm successfully identified the most influential attributes affecting employee performance classification, with achievement, assessment, and service period emerging as key determinants. The resulting decision tree provides a systematic and interpretable classification model that supports objective employee evaluation and talent identification. The study demonstrates that the application of data mining techniques can assist organizations in improving the effectiveness of employee performance assessment and human resource decision-making processes.
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