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All Journal Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation
Klara Bare Nuhan
Universitas Widya Gama Mahakam Samarinda

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Sistem Klasifikasi Kelayakan Penerima Beasiswa menggunakan Algoritma Categorical Naive Bayes Otniel Christovel Ganda; Ridho Aulia Tabliq Sidiq; Bernardo Damian De Ornay; Yovi Aldiyanto; Klara Bare Nuhan; Aldi Bastiatul Fawait
Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation Vol. 1 No. 2 (2026)
Publisher : Universitas Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/minabis.v1i2.29

Abstract

Scholarship selection is often done manually, which takes time and has the potential for errors in assessment. This study aims to build a prediction model for student scholarship eligibility using the Categorical Naive Bayes algorithm. The data used in this study consisted of 1,042 student data with eight attributes: distance from residence to campus, gender, organizational participation, student activity unit (UKM) participation, GPA, parents' occupation, parents' income, and number of dependents. The research method included data preprocessing, feature encoding, data splitting with an 80:20 ratio, model training using three Naive Bayes variants (GaussianNB, CategoricalNB, and ComplementNB), and model evaluation using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results showed that the Categorical Naive Bayes model achieved the best performance with an accuracy of 74.16%, precision of 50.85%, recall of 54.55%, F1-score of 52.63%, and AUC-ROC of 80.25%. The most influential features in determining scholarship eligibility were the number of dependents, GPA, and organizational participation. This study concludes that the Categorical Naive Bayes algorithm can be used to predict scholarship eligibility reasonably well, although it still needs improvement to handle imbalanced data.
Co-Authors Aldi Bastiatul Fawait Fawait Bernardo Damian De Ornay Otniel Christovel Ganda Ridho Aulia Tabliq Sidiq Yovi Aldiyanto