On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java

Authors

  • Nita Cahyani Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo, 60111 Surabaya, Indonesia
  • Kartika Fithriasari Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo, 60111 Surabaya, Indonesia
  • Irhamah Irhamah Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo, 60111 Surabaya, Indonesia
  • Nur Iriawan Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo, 60111 Surabaya, Indonesia

DOI:

https://doi.org/10.11113/matematika.v34.n3.1141

Abstract

Neural Network and Binary Logistic Regression are modern and classical data mining analysis tools that can be used to classify data on Bidikmisi scholarship acceptance in East Java Province, Indonesia. One form of Neural Network model available for various applications is the Resilient Backpropagation Neural Network (Resilient BPNN). This study aims to compare the performance of the Resilient BPNN method as a Deep Learning Neural Network and Binary Logistic Regression method in determining the classification of Bidikmisi scholarship acceptance in East Java Province. After preprocessing data and dividing them into two parts, i.e. sets of testing and training data, with 10-foldcross-validation procedure, the Resilient BPNN and Binary Logistic Regression methods are implemented. The result shows that Resilient BPNN with two hidden layers is the best platformnetwork model. The classificationG-mean resulted by these both methods is that Resilient BPNN with two hidden layers is more representative with better performance than Binary Logistic Regression. The Resilient BPNN is recommended to be used to
predict acceptance of Bidikmisi applicants yearly.

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Published

2018-12-31

How to Cite

Cahyani, N., Fithriasari, K., Irhamah, I., & Iriawan, N. (2018). On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 34(3), 83–90. https://doi.org/10.11113/matematika.v34.n3.1141