<![CDATA[Inactivity is a significant challenge that impacts academic performance, retention rates, and the operational effectiveness of higher education institutions. Royal University faces an urgent need to identify students at risk of becoming inactive early, so that academic interventions can be carried out appropriately and effectively. This study aims to develop a classification model for student inactivity status (Active or Passive) using a machine learning approach, by testing three main algorithms: Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The dataset used consists of 642 student entries, including academic information such as Grade Point Average (GPA), total credits taken, attendance percentage, number of courses per semester, and semester level. The methodology steps include data cleaning and transformation, splitting the dataset into 80% training data and 20% testing data using a random sampling method ( train_test_split with random_state = 42), model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results show that DT and SVM achieve the highest accuracy of 98.44%, with maximum precision in predicting active students, while RF excels in recall (0.96), making it more effective in detecting active students at risk of being missed. Feature importance analysis reveals that GPA and attendance are the most determining factors in predicting student active status, while the number of courses, credits taken, and semester level have a lower additional influence. The primary contribution of this research is the provision of an accurate and practically applicable classification model, enabling universities to conduct automated student monitoring, proactive academic interventions, and data-driven decision-making. Implementing this model in academic information systems can improve the effectiveness of advising programs, reduce the risk of student inactivity , and support efforts to improve retention and graduate quality. This research also emphasizes the importance of contextual features in improving prediction accuracy and provides insights that can be leveraged for the development of data-driven academic strategies]]>
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