Character education through sport is becoming increasingly important in the face of dynamic socio-cultural change. The sociology of sport offers an approach to understanding sport as a social phenomenon that has a broad impact on youth character building. This research uses a descriptive qualitative method to explore the role of sport in youth character building through a sociology of sport approach. The results showed that sport can be a medium for socialization, value internalization, and identity formation. Participation in sports activities can form a physically healthy, mentally strong, and morally sound young generation. However, sports can also be a space for reproduction of negative values if not balanced with constructive social values. Therefore, the sociology of sport approach can offer strategic solutions in designing sports programs that not only pursue achievements, but also form Indonesian people with character. This research is expected to contribute to the development of character education through sports and increase awareness of the importance of the sociology of sport in shaping young people with character and integrity.

This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed that QFE significantly improved LR performance in terms of recall and specificity up to 99%, which is very important in medical diagnosis. The combination of QFE and SMOTE-Tomek gives the best results for the XGB method with an accuracy of 81%, recall of 90%, and f1-score of 83%. This study concludes that the use of QFE and data balancing techniques can improve liver disease classification performance in general.