<![CDATA[Higher education is one way to get job easier, this thing happens because through education the individual is able to increase the level of human resources in this era. However, the high cost of education is very expensive so individuals who wants to study must also work at the same time, so this research aims to predict the student GPA who is studying while working at the same time at Adventist University of Indonesia. From the results of this research there are 8 attributes that have an effect on predicting student GPA at Adventist University of Indonesia, namely the Department of Work, Working Hours, Course, Gender, Residence, Age and Number of Credits. The method that has been used in this research is Decision Tree C4.5 implemented on the WEKA program with the J48 algorithm. This research also uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to balancing the amount of data in the minor class. The top root of this research is Gender which affects the student GPA at University of Indonesia. The SMOTE algorithm in this research is useful to help raising the result of this research by 7-8% can be seen from the results of the accuracy of the cross validation 10 folds test is 63.6672%, the average result of precision and recall are 0.621 and 0.637. While the accuracy of the split test 70:30 is 62.7955%, then result of precision and recall are 0.621 and 0.628. When compared with the use of the Decision Tree C4.5 algorithm only, the accuracy of the cross validation 10 fold test is 55.5044%, with the average result of precision and recall is -.545 and 0.555. While the accuracy of the split test 70:30 is 55.2995% with the results of precision and recall is 0.554 and 0.553. The analysis results using confusion matrix and ROC curve with results from 0.688 to 0.756, which are in the range of 0.70 - 0.80 which is included in the level of fair classification diagnosis. It can be concluded that there is a strong effect while working on the student GPA. With the order of attributes from the top most are Gender, Total Credit, Department, Age, Department of Work, Working Hours and Residence.]]>
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