DOI : https://doi.org/10.18517/ijaseit.11.2.11152

Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University

Ainul Yaqin (1), Arif Dwi Laksito (2), Siti Fatonah (3)
(1) Computer Science Faculty, University of AMIKOM Yogyakarta, Jl. Ringroad Utara, Yogyakarta, 55283, Indonesia
(2) Computer Science Faculty, University of AMIKOM Yogyakarta, Jl. Ringroad Utara, Yogyakarta, 55283, Indonesia
(3) Computer Science Faculty, University of AMIKOM Yogyakarta, Jl. Ringroad Utara, Yogyakarta, 55283, Indonesia
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How to cite (IJASEIT) :
Yaqin, Ainul, et al. “Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, Apr. 2021, pp. 610-7, doi:10.18517/ijaseit.11.2.11152.
Citation Format :
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%.

S. Haryati, A. Sudarsono, and E. (2015) Suryana, “Implementasi Data Mining untuk Memprediksi Masa Studi Mahasiswa Menggunakan Algoritma C4.5,” J. Media Infotama, vol. 11, no. 2, pp. 130-138, 2015.

V. Riyanto, A. Hamid and Ridwansyah, “Prediction of Student Graduation Time Using The Best Algorithm” IJAIDM Indonesian Journal of Artificial Intelligence and Data Mining., vol. 2, no. 1, pp. 1-9, 2019.

A. Nurhuda and D. Rosita, “Prediction Student Graduation on Time Using Artificial Neural Network on Data Mining Students STMIK Widya Cipta Dharma Samarinda,” pp. 86-89, 2017.

A. S. Kurniawansyah, “Implementasi Metode Artificial Hasil Ujian Kompetensi Kebidanan,” J. Pseudocode, vol. V, no. 1, 2018.

S. Asthana, D. Goyal, and A. Pandit, “Analysis on Multiple Hidden Layer Complexity of BPNN,” Int. J. Appl. Eng. Res., vol. 12, no. 14, pp. 4723-4728, 2017.

C. Ahmad, “Implementasi Neural Network Untuk Memprediksi Jumlah Penderita Tuberculosis,” Emit. J. Tek. Elektro, vol. 16, no. 01, pp. 43-50, 2015.

E. Priyanti, “Implementasi Neural Network Pada Prediksi Pendapatan Rumah Tangga,” Swabumi, vol. 6, no. 1, pp. 18-26, 2018.

Efrem Yohannes Obsie and S. A. Adem, “Prediction of Student Academic Performance using Neural Network, Linear Regression and Support Vector Regression: A Case Study,” Int. J. Comput. Appl., vol. 180, no. 40, pp. 39-47, 2018.

MA. Umar, “Student Academic Performance Prediction using Artificial Neural Networks: A Case Study,” International Journal of Computer Applications., vol. 178, no. 48, pp. 24-29, 2019.

Y. T. Samuel, J. J. Hutapea, and B. Jonathan, “Predicting the timeliness of student graduation using decision tree c4.5 algorithm in universitas advent Indonesia,” Proc. 2019 Int. Conf. Inf. Commun. Technol. Syst. ICTS 2019, pp. 276-280, 2019.

S. Wibowo, R. Andreswari, and M. A. Hasibuan, “Analysis and design of decision support system dashboard for predicting student graduation time,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2018-Octob, pp. 684-689, 2018.

S. Agatonovic-Kustrin and R. Beresford, “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research,” J. Pharm. Biomed. Anal., vol. 22, no. 5, pp. 717-727, 2000.

G. Florin, Data Mining:Concepts, Models and Techniques. Romania: Springer, 2011.

D. Puspitaningrum, Pengantar Jaringan Syaraf Tiruan. Yogyakarta: Andi, 2006.

A. Hasim, “Prakiraan Beban Listrik Kota Pontianak Dengan Jaringan Syaraf Tiruan (Artificial Neural Network),” Cent. Libr. Bogor Agric. Univ., p. 1, 2008.

M. T. Hagan, Neural Network Design. USA: PWS Publishing Co, 1996.

F. L, Fundamentals of Neural Networks Architectures, Algorithms and Applications. London: Prantice-Hall, Inc, 1994.

W. J. Stevenson and S. Chee Chuong, Manajemen Operasi Perspektif Asia. Jakarta: Salemba Empat, 2014.

S. B, Data Mining: Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu, 2007.

J. Han, J, Kamber, M, & Pei, Data Mining: Concept and Techniques, Second Edition. Waltham: Morgan Kaufmann Publishers, 2006.

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