<![CDATA[Delays in completing final projects are a common problem faced by students and can lead to delayed graduation, increased study load, and reduced readiness to enter the workforce. This study uses a quantitative predictive approach to analyze the factors influencing delays in completing student final projects by applying the C5.0 Decision Tree classification algorithm. Data were collected through a Likert-scale questionnaire from 204 students of the Faculty of Engineering, University of 17 August 1945 Surabaya, who graduated between 2019 and 2021. The analyzed factors include time management, student motivation, campus policies, faculty support, family support, surrounding environment, and academic skills. The C5.0 algorithm was selected for its higher accuracy and efficiency compared to earlier methods such as C4.5 and CART. The results show that the Surrounding Environment factor is the most dominant, followed by Student Motivation, Time Management, and Family Support. Evaluation of the model yielded excellent classification performance, achieving an accuracy of 95.31%, precision of 96.77%, recall of 93.75%, and an F1-score of 95.24%. These results indicate that the model effectively classifies students at risk of delay with strong predictive reliability. The findings provide insights for universities to develop targeted strategies to enhance student motivation, improve time management, and create a more supportive academic environment. In conclusion, the C5.0 algorithm demonstrates a strong capability to identify dominant delay factors and supports data-driven decision-making in academic management.]]>
