Perbandingan Algoritma Naïve Bayes Dan Regresi Logistik Untuk Memprediksi Kesehatan Mental Mahasiswa Di Provinsi Jambi
DOI:
https://doi.org/10.33998/jakakom.2025.5.2.2309Keywords:
Naïve Bayes, Logistic Regression, Prediction, Mental Health, Academic StressAbstract
he mental health of college students is a global concern due to the impact of academic pressure, social issues,
and lifestyle changes. In Indonesia, around 20% of the population is estimated to suffer from mental disorders, with more
than 12 million people experiencing depression. The increase in suicide cases, including among college students in Jambi,
shows the significant impact of stress on mental health. To address this issue, early prediction of mental health disorders is an
important step so that intervention can be carried out earlier. This study compared the accuracy of the Naïve Bayes algorithm
and logistic regression in predicting the mental health of college students in Jambi Province. Data were collected from 300
students at three different universities. The results showed that Naïve Bayes had an accuracy of 99.58% on the training set
and 100% on the testing set, while logistic regression only reached 61.67% on the training set and 63.33% on the testing set.
These results indicate that Naïve Bayes is superior to logistic regression in predicting the mental health of college students.
These findings can be the basis for the development of more effective early detection tools, so that educational institutions
can design appropriate intervention strategies to support student well-being
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