Comparison of Machine Learning Algorithms in Predicting Student
Perbandingan Algoritma Machine Learning dalam Memprediksi Kelulusan Mahasiswa
DOI:
https://doi.org/10.21070/ups.8509Keywords:
Data Mining, Graduation Prediction, Machine Learning, CRISP-DM, Confusion MatrixAbstract
This study predicts student graduation in the Informatics Study Program at Universitas Muhammadiyah Sidoarjo using Machine Learning algorithms: Naïve Bayes, Decision Tree, and Random Forest. The dataset contains academic records from the 2020–2021 cohort, including GPA and credits (SKS) from semesters 1–6. Data analysis follows the CRISP-DM methodology, covering business understanding, data understanding, preparation, modeling, evaluation, and deployment. Model evaluation uses confusion matrices with accuracy, precision, recall, and F1-score to compare algorithm performance. Results show Random Forest achieved the highest accuracy of 97.50% in the 80:20 scenario, followed by Decision Tree at 96.25% and Naïve Bayes at 86.25%. Random Forest also recorded high precision, recall, and an F1-score of 97%, confirming its stability and effectiveness. Therefore, Random Forest is considered the most optimal algorithm and is recommended as a decision support tool for monitoring and predicting student graduation in higher education.
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