Prediksi Prestasi Belajar Siswa Menggunakan Algoritma Naïve Bayes Classfier di SMKN 1 Barumun
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Date
2023-06
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Elfitra
Abstract
Prediction is an attempt to look at past conditions to predict future conditions. Prediction of
student achievement is something that is very important in the world of education that can increase
quality graduates. This study aims to implement the Naïve Bayes Classifier Algorithm to predict
student achievement for one semester at SMKN 1 Barumun. The data used were 1191 student
data, with a ratio of 80% and 20%, with 6 criteria or attributes for student achievement
requirements consisting of the average grade of assignments, average midterm scores, average
UAS scores, average number of class attendance, average practicum scores and the average
attitude value used for the classification process. Tests were carried out using the Confusion
Matrix with an accuracy of 96.15%, precision of 94,16% and recall of 100%.
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Keywords
Naïve Bayes Classifier Algorithm, Confusion Matrix, Prediction, Achievement
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