Prediction of Student Academic Performance Using Machine Learning Approach
Prediksi Kinerja Akademik Mahasiswa Menggunakan Pendekatan Machine Learning
DOI:
https://doi.org/10.21070/ups.7706Keywords:
Machine Learning, Academic Performance, Naive Bayes, Decision Tree, Support Vector MachineAbstract
One of the most important factors in improving educational standards is predicting students' work habits. The purpose of this research is to use machine learning to evaluate the work habits of university students. Three classification algorithms Naive Bayes, Decision Tree, and Support Vector Machine (SVM) were used to analyze students' work-related data, including midterms, final exams, assignments, and presentations. The data came from the activity log of an e-learning master's program in information studies. The findings showed that the Decision Tree algorithm provided the highest accuracy of 98.51% with 60% of the data used for training and 40% for analysis. The SVM algorithm also performed well with 98.00% accuracy, while the Naive Bayes algorithm achieved 95.99%
accuracy. This research provides insight into the potential of machine learning in assessing students work habits academically and can be used to improve the quality of education and develop systems to detect irregularities
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