Comparative Analysis of Rough Set and Decision Tree Algorithms for Student Graduation Classification
Keywords:
Classification, Student Graduation, Rough Set, Decision Tree, C4.5, Data MiningAbstract
Determining student graduation is an important indicator of educational success. However, the graduation decision-making process is often conducted using conventional methods, which may lead to subjectivity and inconsistency. This study aims to analyze and compare the performance of the Rough Set and Decision Tree (C4.5) algorithms in classifying student graduation based on academic and non-academic attributes. The dataset used in this study includes academic average scores, attendance, behavior, and moral character obtained from historical student data. The Rough Set method is applied to generate decision rules through attribute reduction, while the Decision Tree (C4.5) algorithm is used to construct a classification model based on entropy, information gain, and gain ratio. The results show that both methods are capable of producing accurate classification models, although they differ in terms of model representation and interpretability. Rough Set generates simpler and more interpretable decision rules, whereas Decision Tree provides a hierarchical structure that is easier to visualize and analyze. Therefore, this study contributes to the development of a more objective, transparent, and effective decision support system for student graduation classification.
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