<![CDATA[College is an optional final stage in formal education. At this time, universities are required to have good quality by utilizing all the resources they have. Therefore, efforts are needed to help the faculty and study program make policies and decisions. One of the efforts that can be made is to classify student graduation status as early as possible to increase the number of students graduating on time. Thus, a classification algorithm is needed to avoid overfitting and produce good accuracy. The purpose of this study was to classified the student graduation status of the Statistics Untan Study Program using the XGBoost algorithm. XGBoost is an ensemble algorithm obtained through the development of gradient boosting. XGBoost has several features that can be used to prevent overfitting, but it can only process numerical data. Therefore, 140 numerical data were adjusted using the dummy technique in this study. The resulting XGBoost classification model is optimal at the number of rounds is 3 and the number of folds is 5. Based on the performance evaluation of the XGBoost algorithm, an accuracy of 75,00%, precision of 88,89%, sensitivity of 76,19% and specificity of 71,43% were obtained. Thus, the performance of the XGBoost algorithm is classified as good.]]>
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