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All Journal Jurnal Informatika Upgris JSAI (Journal Scientific and Applied Informatics) Jurnal Teknologi Informasi dan Pendidikan Bulletin of Information Technology (BIT) RJOCS (Riau Journal of Computer Science) Jurnal Informatika Polinema (JIP) Jurnal Teknologi Informasi Indonesia IECON: International Economics and Business Conference
Sulistiawati Rahayu Ahmad
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science

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Analisis Komparatif Algoritma Klasifikasi untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Husain, Hariati; Ahmad, sulistiawati Rahayu; Salim, Muh
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2619

Abstract

- Timely student graduation is an important indicator in assessing the quality of higher education management. However, not all students are able to complete their studies within the prescribed study period, making it necessary to implement data-driven predictive approaches to identify students at risk of delayed graduation. This study aims to compare the performance of the Decision Tree and Naïve Bayes algorithms in classifying timely student graduation based on academic data. The dataset consists of alumni records from the Informatics Engineering Study Program for the 2015–2016 cohorts, totaling 610 valid records after data cleaning and attribute selection. Predictor variables include gender, class type, and Semester Grade Point Index (IPS) from semester 1 to semester 5, while the target variable is graduation status. Model evaluation was conducted using an 80% training and 20% testing split, and performance was measured through a confusion matrix to obtain accuracy, precision, and recall values. The results show that the Decision Tree achieved an accuracy of 69.54%, while Naïve Bayes achieved 68.38%. The 1.16% difference indicates that the Decision Tree performs slightly better for this dataset. These findings suggest that early semester academic performance significantly contributes to predicting timely graduation and can support data-driven academic decision-making.
Co-Authors Abdul Ma’arief Al Imran Danial Saleh Hariati Husain Ichsan M, Muhammad Jorry Karim M Salim M. Salim Marlin Lasena Maro, Muhammad Ihsan Misrawati Aprilyana Puspa Mursyid Ardiansyah Nur Insani R Dalai, Hastuti Saleh, Danial Salim, M Salim, M. salim, Muh Salma P. Nua Syaifuddin Syaifuddin Utiarahman, Siti Andini Zainal Abidin, Ali Asgar