Prediction Of Repeating Object-Oriented Programming Course for Informatics Students at ITAF Kupang Using Extreme Gradient Boosting (XGBoost)
DOI:
https://doi.org/10.62671/perfect.v3i2.285Keywords:
XGBoost, Student Prediction, Learning Behavior, Object Oriented Programming, Machine LearningAbstract
The Object-Oriented Programming course is one of the core courses in the Informatics Study Program which has a fairly high level of difficulty so that some students have the potential to fail and have to repeat the course. This study aims to build a prediction model for students of the Informatics Study Program at ITAF Kupang who have the potential to repeat the Object-Oriented Programming course using the Extreme Gradient Boosting (XGBoost) algorithm based on student learning behavior data. The data used amounted to 60 students with variables including attendance, assignment grades, accuracy of assignment submission, discussion participation, quiz scores, practicum activities, and mid-term/final exam scores. The research stages include data collection, data preprocessing, training and testing data distribution, XGBoost model training, and model evaluation using Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The results of the study showed that the XGBoost model was able to perform good classification with an Accuracy value of 83.33%, Precision of 80.00%, Recall of 80.00%, and F1-Score of 80.00%. Feature importance analysis showed that quiz scores were the most influential factor in students' potential to repeat courses, followed by mid-term/final exam scores and assignment scores. The results of the study proved that student learning behavior data can be used to build an early warning system that helps lecturers and study programs identify at-risk students early on so that more effective academic mentoring can be provided.
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