Naïve Bayes Algorithm for Classification of Student Major’s Specialization

Astia Weni Syaputri(1*), Erno Irwandi(2), Mustakim Mustakim(3)


(1) UIN Sultan Syarif Kasim Riau
(2) UIN Sultan Syarif Kasim Riau
(3) UIN Sultan Syarif Kasim Riau
(*) Corresponding Author

Abstract


Majors are important in determining student specialization. If there is an error in the direction of the student, it will certainly affect the education of subsequent students. In SMA Negeri 1 Kampar Timur, there are two majors, namely Natural Sciences and Social Sciences. To determine these majors, it is necessary to reference the average value of student grades from semester 3 to semester 5 which includes the average value of Islamic religious education, Indonesian, Citizenship Education, English, Natural Sciences, Social Sciences, and Mathematics. Naive Beyes algorithm is an algorithm that can be used in classifying majors found in SMA Negeri 1 Kampar Timur. To determine the classification of majors in SMA Negeri 1 Kampar Timur, training data and test data are used, respectively at 70% and 30%. This data will be tested for accuracy using a confusion matrix and produces a fairly high accuracy of 96.19%. With this high accuracy, the Naive Bayes algorithm is very suitable to be used in determining the direction of students in SMA Negeri 1 Kampar Timur.

Keywords


Confusion Matrix; Classification; Naive Bayes; Student Majors

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References


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DOI: https://doi.org/10.26714/jichi.v1i1.5570

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Journal of Intelligent Computing and Health Informatics (JICHI)
ISSN 2715-6923 (print) | 2721-9186 (online)
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Universitas Muhammadiyah Semarang

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