Optimasi Metode Naïve Bayes Dalam Memprediksi Tingkat Kelulusan Mahasiswa Stekom Semarang

 (*)Edy Siswanto Mail (STEKOM, Indonesia)

(*) Corresponding Author

Abstract

Data about graduating students can provide useful information for universities if used optimally. One way to utilize data about students who are passed is by processing it using data mining. With this data mining process, structures or rules can be found that can be used for information such as predictions of student graduation. From the results of the research conducted from the initial to the final stages by using the naïve bayes method for the prediction process, it was concluded that the methods of applying naïve bayes algorithms used 14 (fourteen) parameters, namely date of entry, class, npm, name, stekom, branch, graduation date, no. diploma, credits, GPA, starting guidance, guidance guidance, study period and student status accuracy rate of 95.14%. Then for graduation prediction using the date of entry the result that students graduate on time is the change date between November 1 and 29

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References

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