According to the Minister of Education and Culture of the Republic of Indonesia's regulations from 2014, one of the essential elements in implementing higher education is the student's study duration. Higher education institutions will use early graduation prediction as a guide when developing policy. According to XYZ University data, the student study period is Grade Point Average (GPA), Gender, and Age are all aspects to consider. Using a dataset of 8491 data, the Prediction of Early Graduation of Students based on XYZ University data was examined by this study, particularly in the information systems and informatics study program. The aim is to find significant features and compare three prediction models: Artificial Neural Networks (ANN), K-Nearest Neighbor (K-NN) method, and Support Vector Machines (SVM). The Challenge in the development of a prediction model is imbalanced data. The Synthetic Minority Oversampling Technique (SMOTE) handles the class imbalance problem. Next, the machine learning models are trained and then compared. Prediction results increase. The best test accuracy value is on ANN with a data Imbalance of 62.5% to 70.5% after using SMOTE, compared to the accuracy test on the K-NN method with SMOTE 69.3%, while the SVM method increased to 69.8%. The most significant increase in recall value to 71.3% occurred in the ANN.
The study period of the student in a tertiary institution is undoubtedly essential in implementing the objectives of the tertiary institution, particularly for the implementation of the study program, so that its outcomes will affect accreditation. Prediction of students' study period can be a reference for higher education institutions in making policies for the future. Based on XYZ University data, especially in the informatics study program, many students have the different generation and concentration therein. In the implementation of students in studying, several factors, including the value of the Grade Point Average (GPA), can affect the study period taken. Likewise, the institutions often do not understand the conditions or predictive value of students' study period on campus. The application of neural networks in predicting the students’ study period at the XYZ University uses a network model with GPA values as input and 1 layer of hidden layers with 10, 50 and 100 neurons; learning rate values used are 0.01, 0.1 and 0.3 and 1 output target for the study period. Prediction results obtained the best results on the neuron network pattern 50 with 0.01 as a learning rate, which detail of MSE value, the training is 0,017516 and the testing is 0,047721, with an accuracy value of 77%.
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