Universitas Sebelas Maret Bidikmisi Applicant's Classification using C4.5 Algorithm
Abstract: Bidikmisi
scholarship is a scholarship for poor but outstanding students. Because of the
amount of applicants, there is a need to use an accurate method in the
selection process of Bidikmisi scholarship, especially in Universitas Sebelas
Maret’s (UNS) environment. In this paper, C4.5 algorithm is proposed as a
method to help on Bidikmisi recipients selection process. The dataset which is
used is Bidikmisi applicants data from 2013 to 2015. The applicant’s data from
2013 and 2014 is used as training data and the applicant’s data from 2015 is
used as testing data. Furthermore, oversampling and undersampling technique is
used to address the class imbalance problem in training data. Finally the
accuracy for each decision trees are compared to see which sampling method is
better. The result of this study shows that the accuracy of the C4.5 algorithm
decision tree with the applicant’s data from 2015 as testing data is 79,80% and
Area Under Curve (AUC) value 0.5539. Meanwhile, to compare the sampling method,
the best decision tree based on testing result is chosen. Oversampling
technique produce 82,69 % for precision, 91,22 % for recall, and 77,16 % for
accuracy. While undersampling technique produce 82,78 % for precision, 91,22 %
for recall, and 77,27 % for accuracy. Therefore it is concluded that
undersampling technique gives a better accuracy than oversampling technique.
Penulis: Muh. Safri Juliardi
Kode Jurnal: jptinformatikadd170128
