Application of Data Mining Techniques in Assessing the Performance of Vocational High School Students in Computer Engineering at SMK Negeri 1 Braja Selebah Using Support Vector Machines (SVM), Naive Bayes, and k-Nearest Neighbors (k-NN) Algorithms - Putra, Yoga Adi; Nugroho, Handoyo WD; Chairani, C; Trilokai, Joko (LPPM STIKOM Tunas Bangsa, 2024)
This study discusses the performance comparison of three classification algorithms: K-Nearest Neighbors (k-NN), Naive Bayes, and Support Vector Machines (SVM), in assessing student performance at a Vocational High School specializing in Computer Engineering. The objective of this research is to identify the most effective algorithm for classification based on various evaluation metrics such as accuracy, precision, recall, and F1-Score. The experimental results show that the SVM algorithm has the best performance with an accuracy of 93.2%, precision of 93.4%, recall of 93.2%, and F1-Score of 93.1%. Naive Bayes ranks second with an accuracy of 86.2%, precision of 86.8%, recall of 86.2%, and F1-Score of 86.4%. The k-NN algorithm is in the last position with an accuracy of 81.0%, precision of 81.0%, recall of 82.0%, and F1-Score of 80.0%. Therefore, the SVM algorithm is recommended as the best model for classification in the context of this research.