<![CDATA[Monitoring student academic status is an important aspect of supporting the effectiveness of higher education systems. This study aims to classify student status—Dropout, Enrolled, and Graduate—using the XGBoost algorithm on an academic dataset from Kaggle consisting of 4424 entries and 37 features. The research was conducted using a quantitative approach and computational experiment methods. The stages included data pre-processing, training and testing data split (80:20 ratio), model training, and evaluation using classification metrics such as accuracy, precision, recall, and F1-score. The results showed that the XGBoost model was able to classify student status with an accuracy of 79.9%. Further evaluation revealed that the model performed well on the majority class (Graduate), but had low performance on the minority class (Enrolled), especially with recall of 0,48. This indicates challenges in dealing with imbalanced class distributions. Therefore, the use of data balancing techniques must be implemented, thus the accuracy by the model reached 82,65%, for further refining and improving its performace, hyperparameter tuning is recommended for it. The results of this study are expected to assist educational institutions in detecting potential dropouts earlier and supporting data-driven decision making.]]>
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