Penerapan Algoritma C4.5 untuk Klasifikasi Tingkat Kedisiplinan Siswa Sekolah Menengah
DOI:
https://doi.org/10.47065/jieee.v5i1.2630Keywords:
Data Mining; Classification; Decision Tree; C4.5; ; Model Evaluation; Student PerformanceAbstract
Abstract?This study aims to evaluate the performance of the Decision Tree algorithm based on the entropy criterion (C4.5) in classifying student eligibility by considering both academic and non-academic data. The dataset consists of 200 entries with nine attributes, including attendance percentage, number of lateness incidents, disciplinary violations, average academic scores, participation, study hours, and extracurricular activities. Data processing was carried out through several stages, namely cleaning, transformation, feature selection, training and testing data splitting, and model evaluation using a confusion matrix. The experimental results show that the proposed model achieved an accuracy of 87.5%, an average precision of 85.6%, an average recall of 84.2%, and an F1-Score of 84.8%. These findings confirm that the C4.5 algorithm can be effectively applied to support student performance classification with a fairly high level of reliability.
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