KLASIFIKASI PRESTASI AKADEMIK PESERTA DIDIK DENGAN METODE MACHINE LEARNING DI SMP X - Fandi Chriswantoro Putro; Ahmad Chusyairi; Cian Ramadhona Hassolthine (Jurnal Teknologi Informasi dan Komputer, 2025)
Machine learning (ML) is a field of science that focuses on designing and developing algorithmic models to create behavior based on available data. Academic achievement is a metric used for the assessment of quality educational institutions. By using academic data of students in SMP X and machine learning classification algorithms such as Random Forest, NaÃ¯ve Bayes, k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM), so that this research can classify the academic achievement of students in SMP X optimally seen from the comparison of the best accuracy rate among classification algorithms. The accuracy of an algorithm is a measure of how precisely it classifies a sample. Evaluation results are compared in the form of validation accuracy and standard deviation. The comparison is done to determine the best algorithm based on accuracy and stability. The results showed that the SVM algorithm has the highest validation accuracy with a value of 0.987410 which shows the best performance in predicting classes and the lowest standard deviation value of 0.005132 which shows a more stable and consistent performance, compared to other algorithms. This indicates that SVM excels in predicting the correct class with stable performance. Based on the results and analysis, it is concluded that the selected SVM algorithm is used to develop a classification model of students' academic achievement in the form of a python program that is still simple but has high accuracy, stable and consistent. This program has become a tool for SMP X in identifying students' academic achievement and as a material for reporting students' learning outcomes to parents.