Peningkatan Akurasi Prediksi Performa Akademik Siswa Menggunakan Model Stacking Ensemble - Ari Nugroho Putro; Much. Aziz Muslim (Prosiding SISFOTEK, 2025)
Educational data mining has become an effective tool for exploring and predicting student academic performance. Various studies have shown its potential in developing early detection systems for students at risk of dropping out of school. However, the main challenge in such prediction systems is the low performance of conventional classification algorithms in producing high accuracy. This study aims to improve the accuracy of student performance predictions by applying a stacking ensemble model that combines several algorithms. The model developed uses two base learners, namely XGB, LGBM, SVM, and LR, which are then combined through the meta learner LR to produce a final decision. The experiment was conducted using a dataset predicting student dropout and academic success, which included academic paths, demographics, socioeconomic status, and academic performance of students in their first and second semesters. Model validation was performed using 10-fold cross validation to ensure the stability and generalization ability of the model. The test results showed that the stacking ensemble model achieved an accuracy of 0.9168, superior to the single classification model. These findings prove that the stacking ensemble approach is effective in improving student performance predictions.