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All Journal Experimental Student Experiences
Putriyana Putriyana
STMIK IKMI Cirebon
Arts Humanities Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Implementation of Data Mining to Predict Graduation of SMK Al Huda Kedungwungu Students Using the Naïve Bayes Classifier Algorithm Putriyana Putriyana; Odi Nurdiawan
Experimental Student Experiences Vol. 1 No. 4 (2023): April
Publisher : LPPM Sunan Doe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58330/ese.v1i4.202

Abstract

The purpose of prediction is to become decision makers and make policies. Understanding the uncertainties and risks that may arise can be considered when making plans. By making these predictions, planners and decision makers will be able to consider other alternatives, so they can take advantage of student graduation data. The algorithm that will be used is the Naive Bayes Classifier Algorithm which is a simple probability classification method based on the application of Bayes' theorem with the assumption that explanatory variables are independent, clues and supporting data in predicting student graduation, namely student behavior, school exams, grades. In practice, the application of the Naive Bayes method applies data train to produce the probability of each criterion for different classes, so that the probability value of these criteria can be optimized to determine predictions of student graduation quickly and efficiently based on the classification carried out using the Naive Bayes method, then from the results of testing with the Naive Bayes method the results obtained an accuracy value of 76 .25%, so this result has very good accuracy. That way this method can be applied in predicting student graduation.
Co-Authors Odi Nurdiawan