Predicting Student Preparation for Exams Using the Naive Bayes Method
Abstract
The National Examination (UN) is a government policy in the field of education to determine the quality standards of educatioN. The function of the national examination is important to measure student competence and one of the considerations for selection to a higher level. The Naive Bayes algorithm is mostly used in spam message filtering, sentiment analysis, and recommendation systems. One of the main reasons for the use of this algorithm is due to its quick and easy implementation. The test was carried out using the WEKA application, using the confusion matrix method to determine the effectiveness of classification with model equations and can be calculated to find accuracy, sensitivity, specificity, positive predictive value (ppv), and negative predictive value (npv). The results of the evaluation and validation of the confusion matrix using training data and testing data showed the accuracy rate and error rate in the Naïve Bayes algorithm of 96.8254% and 3.1746%.
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