CLASSIFICATION OF STUDENTS' ACADEMIC STRESS LEVELS THROUGH DATA MINING TECHNIQUES BASED ON THE C4.5 ALGORITHM
DOI:
https://doi.org/10.30656/y35a5z47Abstract
Academic stress is a common psychological problem experienced by students during their education at university. This condition can be influenced by various factors, including high academic load, limited time to complete assignments, difficulty understanding lecture material, and an unsupportive learning environment. If not managed effectively, academic stress has the potential to negatively impact students' academic achievement, learning motivation, and mental health. Along with the development of information technology, the application of data mining techniques can be utilized to help identify and classify students' academic stress levels more objectively and accurately. This study aims to classify the academic stress levels of using the C4.5 algorithm. Research data were obtained through distributing questionnaires to students from the Informatics Engineering, Information Systems, and Computer Engineering Study Programs. The research stages include data preprocessing, the formation of a classification model, and evaluation of model performance. Algorithm was chosen because it has the ability to build a classification model in the form of a decision tree that is easy to interpret and is able to determine the most influential attributes in the decision-making process. The resulting classification model is expected to be a tool for universities, especially lecturers and student affairs, in conducting early detection of students who have the potential to experience academic stress. Thus, appropriate mentoring and intervention steps can be provided to support the academic success and psychological well-being of students.
Keywords: Academic Stress ,C4.5 Algorithm, Classification,Data Mining, Students
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