<![CDATA[Sleep patterns refer to an individual’s habits in managing sleep and wake times, including duration, quality, and regularity. Students, particularly those in the Informatics Engineering Program at Universitas Harapan Medan, often experience irregular sleep patterns due to heavy academic workloads such as assignments, projects, and practical activities. This condition can reduce academic productivity in terms of concentration, memory, and the ability to complete tasks on time. Therefore, this study aims to develop a classification model to predict student productivity levels based on sleep patterns using the Decision Tree C4.5 algorithm. This algorithm was chosen for its advantages in interpretability, ability to handle both numerical and categorical data, and efficient attribute selection, which contribute to generating an accurate and transparent classification model. The study involved 30 respondents from the 8th semester of the Informatics Engineering Program at Universitas Harapan Medan in the 2024/2025 academic year who filled out questionnaires regarding their sleep patterns and productivity. The results showed that 15 respondents (41.2%) had low productivity, 9 respondents (35.3%) had medium productivity, and 6 respondents (23.5%) had high productivity. These findings indicate a significant relationship between sleep pattern regularity and student productivity levels. The model generated using the C4.5 algorithm is expected to serve as a foundation for developing decision support systems aimed at improving the balance between sleep patterns and academic productivity among students.]]>
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