Pengembangan Model Untuk Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes

 M Riski Qisthiano (Universitas Bina Darma, Palembang, Indonesia)
 (*)Tri Basuki Kurniawan Mail (Universitas Bina Darma, Palembang, Indonesia)
 Edi Surya Negara (Universitas Bina Darma, Palembang, Indonesia)
 Muhammad Akbar (Universitas Bina Darma, Palembang, Indonesia)

(*) Corresponding Author

Submitted: May 30, 2021; Published: July 31, 2021

Abstract

Many parameters affect the timeliness of student graduation, starting from the student's interest in certain majors, the type of class chosen, to the grades for each semester obtained. This is a determining factor in how students can graduate on time or not at the end of their education. So a model is needed to predict student graduation rates on time, using alumni data whose data is obtained from several universities in Palembang City. The model used is a Naïve Bayes algorithm which serves as a model for classification. The dataset used is alumni data that has been collected from several universities, while the attributes used are the Department, College, Class Type, Temporary IP Value from semester 1 to 4, graduation year, and college generation. Then from the attributes and models used, the researcher used the Python 3 programming language and the Jupyter Notebook tools to process the prepared dataset. Furthermore, the distribution of the dataset is divided by 70% for training data and 30% for testing data. To test the algorithmic process used by researchers using K-Fold Validation. The results of this study are the accuracy of the prediction model carried out, where the accuracy results obtained from the Python 3 programming language and the Naïve Bayes algorithm are 0.8103.

Keywords


Naïve Bayes; Python 3; University; Student; Alumni

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