Implementation of the C4.5 Algorithm to Build A Prediction Model for Student Success in Database Courses - Nanda Pratama Alfyandri; Hari Antoni Musril; Sarwo Derta (Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi, 2025)
This study aims to implement the C4.5 algorithm to build a model for predicting student success in database system courses in the Informatics and Computer Engineering Education study program at UIN Sjech M. Djamil Djambek Bukittinggi. Using the Knowledge Discovery in Database (KDD) approach, this study includes the stages of data selection, cleaning, transformation, modeling, and evaluation. Secondary data from the academic information system of students enrolled from 2018 to 2023 included 1,177 entries, which after cleaning resulted in 1,030 valid data. Predictor attributes consisted of academic factors such as Algorithm Logic scores, 1st semester Grade Point Average (GPA), attendance, and credit load, as well as non-academic factors such as gender and UKT (Tuition Fee Category). The target variable was student success status. Modeling was performed using Altair RapidMiner 2025 software with the C4.5 algorithm, resulting in a decision tree model. Evaluation showed an accuracy of 82.10%, recall of 69.58%, and precision of 62.51%, indicating the algorithm's effectiveness in classifying students as potentially successful or unsuccessful. This model identifies the most influential attributes, both academic and non-academic, on student success. Overall, the application of the C4.5 algorithm supports Educational Data Mining (EDM) in higher education, helping study programs improve the quality of learning and the effectiveness of data-based academic interventions.