Analysis of Student Dropout Potential Using the Multinomial Naive Bayes Algorithm

Dewi Afrianti; Armansyah Armansyah

doi:10.24014/ijaidm.v7i2.32316

Analysis of Student Dropout Potential Using the Multinomial Naive Bayes Algorithm

Dewi Afrianti, Armansyah Armansyah

Abstract

The current situation of education in Indonesia is quite concerning, especially with the high dropout rate which is one of the main problems. The variation in dropout rates in various educational institutions, including at Muhammadiyah 9 Vocational High School in Medan, reflects the diversity of challenges faced. This study aims to analyze the supporting factors that influence the potential for student dropout using the Naive Bayes Multinomial method, especially at this school. The results of the study showed that the model could understand the data with a classification performance accuracy of 83.04% at the 20% dataset testing stage. Through this test, 76 active students, 11 students with the potential to drop out, and 25 students dropped out were obtained. Meanwhile, precision, recall, and f1-score in the class with the potential to drop out cannot be displayed because the class comparison is unbalanced.

Keywords

Accuracy; Classification Model; Multinomial Naive Bayes; Potential Dropout; Supporting Factors

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DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.32316

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