<![CDATA[This study examines the classification of Depression Status among students using a data mining approach based on the C4.5 Algorithm. The primary goal is to construct a predictive model that incorporates two essential variable groups: Academic Patterns (Academic_Performance, Taking_Note_In_Class, Face_Challenges_To_Complete_Academic_Task, Like_Presentation) and Student Lifestyle (Sleep_Per_Day_Hours, Number_Of_Friend, Like_New_Things). Depression Status is recognized as a significant mental health issue triggered by both external and internal pressures—most notably Academic Stress, arising when learning demands exceed students’ capabilities, and further intensified by unhealthy lifestyle habits, such as poor time management and excessive or unregulated use of social media. These conditions collectively undermine psychological well-being and negatively affect academic performance. The C4.5 Algorithm is employed due to its high classification accuracy and the clarity of its output through interpretable decision trees. The resulting model categorizes Depression Status into three classes: Yes, Sometimes, and No, while also identifying the most influential contributing factors. The final outcomes of this classification model are expected to generate reliable predictions that can serve as a scientific foundation for teachers, school counselors, and parents in designing more targeted and effective programs for the prevention and intervention of student depression.]]>
