Perbandingan Metode Klasifikasi Naive Bayes Dan K-Nearest Neighbor Dalam Memprediksi Prestasi Siswa

Abstract

Student achievement is one of the most important aspects in the field of education, so it is necessary to take action to improve the quality of education in dealing with students who have less achievement so that teachers can identify students who need more guidance. This study aims to predict student achievement by processing students' report cards using the Naïve Bayes classification and k-Nearest Neighbor data mining method approach, and utilizing orange software as a testing tool. The data used is data on grade 1 student scores for the 2019 to 2022 academic year totaling 143 data. The evaluation results using test and score yield CA (Classification Accuracy) values ​​for the Naïve Bayes algorithm 0.909 and the k-NN algorithm 0.848, the F1 value for the Naïve Bayes algorithm 0.858 and the k-NN algorithm 0.820, the Precision value for the Naïve Bayes algorithm 0.862 and the k-NN algorithm 0.821 the Recall value of the Naïve Bayes algorithm is 0.857 and the k-NN algorithm is 0.820. The Naïve Bayes AUC value is 0.909 and the k-NN value is 0.848. Therefore, for the case study, the ROC analysis model that has the best accuracy value is Naïve Bayes because the curve is closest to the coordinate point of 0.1. Based on the analysis of each of these values, the amount of False Negative and False Positive data is close to (Symmetric) and the accuracy value is very high, so accuracy can be used as a reference for algorithm performance. With this research, it is hoped that it will make it easier to determine student potential and can help improve the learning system for students who have difficulty in achieving achievement.