<![CDATA[Timeliness of graduation not only reflects the competence of graduates but also affects the assessment of study programme accreditation. To achieve this goal, it is important to predict and classify the timeliness of graduation to support more effective academic decision making. In this research, the Knowledge Discovery in Database (KDD) process is used, which aims to find knowledge from big data. One of the main stages in KDD is data mining, which focuses on pattern extraction with various algorithms. This research uses the C4.5 algorithm, a classification method that builds a decision tree to identify attributes that affect the timeliness of student graduation. This study uses data from students in 2017, 2018, and 2019 from the Bachelor of Nursing and Bachelor of Public Health study programmes at Syedza Saintika University, with a total sample of 46 student records. The C4.5 algorithm is applied to form a decision tree model, which produces classification rules based on attributes such as Grade Point Average (GPA), Study Programme, Gender, and Region of Origin. The results of the C4.5 algorithm implementation show a prediction accuracy of 89.13%, with GPA as the most dominant factor in influencing graduation accuracy. This research proves that the C4.5 algorithm is effective in predicting the timeliness of student graduation.]]>
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