Implementasi Naïve Bayes Classifier pada Sistem Informasi untuk Klasifikasi Ketepatan Kelulusan Santri - Pramuditha Shinta Dewi Puspitasari; Taufiq Rizaldy; Faisal Lutfi Afriansyah; Zhaqian Rouf Alfauzi (Journal of Information System Research (JOSH), 2026)
Pondok Pesantren Mahasiswa (PPM) is an educational model that integrates higher education with the Islamic boarding school system to produce graduates with strong academic competence and religious character. However, the dual demands of academic and boarding school activities increase the risk of delayed graduation among students, highlighting the need for early intervention to prevent study failure. This study aims to develop a web-based system that classifies students' on-time graduation using the Naïve Bayes algorithm. The dataset was obtained from PPM archives and consisted of 211 student records with attributes including gender, region of origin, study duration, Al-Qur'an achievement, and Al-Hadith achievement. The proposed system classified 172 students as likely to graduate on time and 39 students as likely to graduate late. Performance evaluation using a confusion matrix achieved an accuracy of 86.79%, a precision of 93.41%, and a recall of 91.40%, indicating good classification performance. Furthermore, User Acceptance Testing (UAT) showed high user satisfaction, with acceptance rates of 95.66% from students and 84% from boarding school administrators, both categorized as excellent. The main contribution of this study is the integration of academic and Islamic boarding school attributes, particularly Al-Qur'an and Al-Hadith achievements, into a Naïve Bayes classification model and its implementation in a web-based system to support monitoring, evaluation, and decision-making related to students' on-time graduation.