Analyzing Student Academic Achievement Using Machine Learning Techniques at Senior High School Darunnajah Jakarta - Panggabean, Supriadi; Wahyu Joko Saputro (Inspiration: Jurnal Teknologi Informasi dan Komunikasi, 2024)
Education provides a very important role in improving the quality of life in society in a country. With a large number of students in each class, it can cause the material to not be delivered properly. Therefore, it is necessary to group students based on their learning ability. The data used was obtained from Senior High School (SMA) Darunnajah Jakarta. Darunnajah High School Jakarta is one of the educational institutions under the auspices of Darunnajah Islamic Boarding School. Data mining techniques with classification methods are proposed to predict student performance in class. The results of student classification can be used as a reference in providing material according to their learning ability. The aim of this research is to ascertain the optimal classification algorithm and pinpoint the key factors influencing students' academic standing. Various classification methods, including logistic regression, KNN, and SVM, were employed in this study. The performance of these models was assessed using diverse metrics such as the f1 score, ROC curve, and performance matrix. Ultimately, the SVM algorithm demonstrated the highest accuracy, achieving an 84% accuracy rate compared to KNN and logistic regression.