Implementation of Data Mining for Predicting Graduation of Industrial Engineering Students at Telkom University Using Naïve Bayes
Abstract
In higher education, students play a central role, and their academic progress is essential for Telkom University's evaluation. To improve graduation prediction accuracy, the Head of the Study Program is implementing the Naïve Bayes method through a dashboard. This method considers attributes like gender, semester grades (IPS1 to IPS6), and graduation status, achieving an 83.11% accuracy rate. The designed dashboard not only streamlines monitoring but also allows for intervention and improvement strategies, leading to enhanced learning outcomes and better overall academic management effectiveness. Keyword— Data Mining, Naïve Bayes Classifier, Student GraduationIn higher education, students play a central role, and their academic progress is essential for Telkom University's evaluation. To improve graduation prediction accuracy, the Head of the Study Program is implementing the Naïve Bayes method through a dashboard. This method considers attributes like gender, semester grades (IPS1 to IPS6), and graduation status, achieving an 83.11% accuracy rate. The designed dashboard not only streamlines monitoring but also allows for intervention and improvement strategies, leading to enhanced learning outcomes and better overall academic management effectiveness.
Keyword: Data Mining, Naïve Bayes Classifier, Student Graduation
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