Analysis Of The Decision Tree (C4.5) And Random Forest Algorithms To Determine Student Eligibility For Final Project Assignments Based On Academic Requirements - Eisyaniah Desvazulinda; Muhammad Iqbal; Muhammad Syahputra Novelan (Universitas Budi Darma, 2026)
Determining student eligibility for undertaking a final project is an important process in higher education, which is often still conducted manually and subjectively. This study aims to develop a classification model based on machine learning to determine the eligibility of students at Batam University using Decision Tree (C4.5) and Random Forest algorithms. The data used includes Grade Point Average (GPA), total completed credits (SKS), prerequisite course grades, and academic records. This research employs a quantitative approach with stages including data collection, data preprocessing, model development, and performance evaluation using accuracy, precision, and recall metrics. The results show that both algorithms are capable of classifying student eligibility effectively. The Decision Tree (C4.5) algorithm produces an interpretable model in the form of decision rules, while Random Forest demonstrates superior performance in terms of accuracy and prediction stability. The comparison indicates that Random Forest is more effective in handling complex data, whereas C4.5 provides better model transparency. In conclusion, the implementation of Decision Tree (C4.5) and Random Forest algorithms can serve as an effective solution to support objective and data-driven academic decision-making. The resulting model has the potential to be developed into a decision support system to improve the efficiency and quality of determining student eligibility for final project enrollment.