Prediction of Student Graduation Using Naïve Bayes

Robi Sepriansyah, Susan Dian Purnamasari

Abstract


The quality of universities, especially study programs in Indonesia is measured based on an accreditation assessment from the National Accreditation Board for Higher Education (BAN-PT). The quality assessment is measured based on 7 main standards, one of which is students and graduation. Every university must have academic data and biodata of each student based on the initial registration until graduation. Students who are accepted or who enter college are increasing every year, but not all students are able to graduate on time.algorithm Naïve Bayes used study aims to predict student graduation through student academic performance data in semester one to semester four, attributes Nim, Credit and GPA using the Discovery In Database (KDD) This Knowledge model data Testing on the Rapid Miner application.From the results of the tests that have been carried out, it can be concluded that the accuracy value of the prediction results is 95.33%, the results are quite accurate for the data used by testing the testing as many as 120, namely passing in semester 8 as many as 78, passing in semester 9 as many as 24, while 3 who graduated in semester 10, and students who graduated in semester 12 were 15.


Keywords


prediction; classification; naive bayes; rapid miner

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References


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DOI: https://doi.org/10.33258/birci.v5i3.6447

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.