Ensemble learning techniques to improve the accuracy of predictive model performance in the scholarship selection process

Abstract

Ensemble Learning is an algorithm that searches for the best prediction result based on several classifier solutions which are come from different algorithms. Ensemble learning has better accuracy and performance compared to other algorithms because this method uses several learning algorithms to achieve better predictive solutions. There are a lot of data that the scholarship organizer receives and manages. This makes the process take a lot of time to do checking process and makes it inefficient. Accelerating the process while also maintaining the accuracy of the scholarship selection process can certainly improve the selection performance. In this study, we process student data from UIN Jakarta University as a sample. The model will have 2 base classifiers, namely Support Vector Machine (SVM) and Key Nearest Neighbor (KNN). Each of these algorithms already has quite a good accuracy. Using Ensemble Learning improves the model performance because it has the ability to overcome errors that occur in each data prediction. We can exploit the classification application created using "Streamlit" and will determine whether a student is accepted or rejected in the scholarship selection process. Our model and application can be used by academics as a Decision Support System (DSS) for determining scholarship recipients. This model can also be used as a development for institutions in the academic field.