Denis Eka Cahyani, Elizabeth Paskahlia Gunawan, Langlang Gumilar
Bidikmisi scholarships are tuition assistance for prospective students who are economically disadvantaged but have the good academic potential to study at State Universities in Indonesia. With the amount Bidikmisi applicants, there is a need to use a system that can speed up the selection process and avoid human errors. The purpose of this study is to develop a classification system for Bidikmisi scholarship applicants at Universities in Indonesia. This study uses a multilayer perceptron (MLP) neural network to build a classification model. Because MLP has some hidden layers, MLP has advantages to predict or analyze complex problems. The methodology in this study is data collection, data preprocessing, data encoding, data modeling, and evaluation. The results of the classification of bidikmisi scholarship applicants are divided into two classes, namely accepted and rejected. The multilayer perceptron method generates an accuracy value of 72.81 %. The Multilayer perceptron is compared with other classification methods, namely Naive Bayes and K-Nearest Neighbors (KNN). The results of the classification using the Naive Bayes method obtained an accuracy value of 79.47%, while the accuracy value using KNN was 78.33%. Evaluation using MLP resulted in precision, recall, and Fl-measure values of 68.36%, 72.81%, and 66.33%, the results of the evaluation using Naive Bayes were 75.58%, 79.47%, and 71.24%, while the KNN values were 71.90%, 78.33%, and 72.53%. In order to the conclusion in this research is the MLP method can generate good performance for the classification of bidikmisi scholarship applicants. However, Naive Bayes' performance is better than MLP for the classification of bidikmisi scholarship applicants. © 2022 American Institute of Physics Inc.. All rights reserved.
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, Malang, Indonesia; Department of Informatics, Universitas Bina Nusantara, Malang, Indonesia; Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Malang, Malang, Indonesia