Prediksi Kelulusan Mahasiswa Strata 1 (S1) Menggunkan Metode C5.0 di Program Studi Ilmu Komputer
-
Mubarak Ba’ayesh
*
Universitas Islam Negeri Sumatera Utara, Medan,
Indonesia
- Ilka Zufria Universitas Islam Negeri Sumatera Utara, Medan, Indonesia (*) Corresponding Author
Abstract
Timely graduation is a crucial indicator of the success of study programs in higher education. However, in the Computer Science Study Program at UIN Sumatera Utara, the number of students graduating on time is relatively low. This study aims to predict student graduation using the C5.0 algorithm, a part of Decision Tree, to classify students who graduate on time and those who graduate late. The data used includes GPA scores from semesters 1 to 4, total credits (SKS), final project duration, study period, and entry pathway. From 100 data samples, the model was tested using a 70:30 data split ratio. The evaluation results showed that the prediction model using the C5.0 algorithm achieved 100% accuracy, with Precision, recall, and f1-score values of 1.00 for both classes, namely "On Time" and "Late." This research demonstrates that the C5.0 algorithm can accurately predict student graduation and assist the university in developing strategies to improve timely graduation rates.
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