Perbandingan Algoritma Naïve Bayes Dan Regresi Logistik Untuk Memprediksi Kesehatan Mental Mahasiswa Di Provinsi Jambi - Fitriani; Jasmir; Sharipuddin (Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM), 2025)
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 morethan 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 animportant step so that intervention can be carried out earlier. This study compared the accuracy of the Naïve Bayes algorithmand logistic regression in predicting the mental health of college students in Jambi Province. Data were collected from 300students at three different universities. The results showed that Naïve Bayes had an accuracy of 99.58% on the training setand 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 institutionscan design appropriate intervention strategies to support student well-being