Klasifikasi Kemampuan Mahasiswa Berdasarkan Automatic Essay Scoring terhadap Jawaban Essay Ujian Kompetensi dengan Metode Machine Learning

Hakiki, Muhammad
Fatichah, Chastine

Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Vol. 6 No. 3 (2025): September

Manually assessing student answers and grouping student abilities is very time-consuming. Therefore, a system is needed that can automatically assess student essay answers and group student abilities. This study proposes a method for classifying student abilities based on the Automatic Essay Scoring value using the LSTM method and several classification methods. The number of datasets used in this study was 98 students, while the questions tested in this competency exam were 200 questions. The parameters used for LSTM are student answers. The benefit of this study is to find out which students have mastered the lecture and which students have not mastered the lecture. The results of this study indicate that the LSTM method successfully provides automatic essay assessment with an accuracy value of 0.9, while the most superior classification method is the Decision Tree method with the ROS oversampling method, which is 0.654.