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Sleep is a fundamental human need that plays a crucial role in maintaining both physical and mental health. Poor sleep quality can trigger a variety of health problems, ranging from decreased concentration to an increased risk of chronic diseases. The complexity of factors influencing sleep quality—such as stress levels, heart rate, blood pressure, physical activity, and lifestyle—makes its assessment difficult through direct observation alone. Therefore, data mining approaches are increasingly utilized to identify relevant patterns in sleep-related data. This study aims to compare the performance of the C4.5 (Decision Tree) algorithm and the Naïve Bayes algorithm in predicting sleep quality using the Sleep Health and Lifestyle dataset, which contains information from 374 respondents. The research method applied is a quantitative comparative approach employing classification techniques with 10-fold cross-validation to ensure robust evaluation. Model performance is assessed using accuracy, precision, and recall metrics to provide a comprehensive understanding of the effectiveness of each algorithm. The findings indicate that the C4.5 algorithm achieves an accuracy of 96.26% and offers advantages in terms of interpretability through its decision tree visualization, enabling easier understanding of variable relationships. In contrast, the Naïve Bayes algorithm demonstrates superior predictive performance, achieving an accuracy of 98.66% along with consistently high precision and recall across nearly all classes. These results suggest that Naïve Bayes is more effective for predictive tasks involving sleep quality, while C4.5 remains highly valuable when the goal is to interpret variable interactions and decision rules. Overall, this research highlights the potential of data mining techniques in health informatics, particularly in improving the understanding and prediction of sleep quality, which in turn can contribute to better prevention and management of sleep-related health issues.

Education is a fundamental aspect in shaping the future of the younger generation, and choosing further studies after education at junior high school level is a crucial decision. At UPTD SMP Negeri 10 Kupang, many students, especially in class IX for the 2024/2025 academic year, face difficulties in determining their further study path. To help students plan their careers, implementing Guidance and Counseling (BK) services in the career field is very necessary, one of which is through creating a career tree. A career tree is a visual aid that depicts the career paths and educational pathways that students can pursue based on their interests and abilities. This method aims to provide students with a better understanding of the career options available, recognize their own potential, and plan strategic steps to achieve their career goals. The results of this activity show that making a career tree was successful in providing insight to students, helping them know themselves better, and motivating them to actively seek information about careers and further education. Creating a career tree has proven to be effective as a solution in planning the right career and further studies. Therefore, it is recommended to improve guidance and counseling services, implement career trees regularly, and involve parents and the community in supporting students' career development.