Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm
DOI:
https://doi.org/10.29244/ijsa.v5i2p396-404Keywords:
bidikmisi scholarship, genetic algorithm, neural networkAbstract
A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.
Downloads
References
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.
Han, J., & Kamber, M. (2006). Concepts and techniques. Morgan Kaufmann, 340: 94104–3205.
Haupt, R. L., & Ellen Haupt, S. (2004). Practical genetic algorithms.
Haykin, S. (1999). Self-organizing maps. Neural Networks-A Comprehensive Foundation, 2nd Edition, Prentice-Hall.
Karegowda, A. G., Manjunath, A., & Jayaram, M. (2011). Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes. International Journal on Soft Computing, 2(2): 15–23.
Khan, A. U., Bandopadhyaya, T., & Sharma, S. (2008). Genetic algorithm based backpropagation neural network performs better than backpropagation neural network in stock rates prediction. International Journal of Computer Science and Network Security, 8(7): 162–166.
Last, M. (2006). The uncertainty principle of cross-validation. 2006 IEEE International Conference on Granular Computing, 275–280. IEEE.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088): 533–536.
Sadeq, A., Wahdan, A.-M. A., & Mahdi, H. M. (2000). Genetic Algorithms and its use with back-propagation network. AIN Shams University Scientific Bullettin, 35(3): 337–348.
Sri, K. (2004). Membangun Jaringan Syaraf Tiruan Menggunakan matlab & Excel Link. Graha Ilmu, Yogyakarta.
Trevino, V., & Falciani, F. (2006). GALGO: an R package for multivariate variable selection using genetic algorithms. Bioinformatics, 22(9): 1154–1156.
