METODE KLASIFIKASI JARINGAN SYARAF TIRUAN BACKPROPAGATION PADA MAHASISWA STATISTIKA UNIVERSITAS TERBUKA

  • Siti Hadijah Hasanah Universitas Terbuka
  • Sri Maulidia Permatasari Universitas Terbuka
DOI: https://doi.org/10.30598/barekengvol14iss2pp249-258
Keywords: backpropagation, cut off point, activation function, artificial neural network, classification

Abstract

Backpropagation Artificial Neural Network (ANN) is an ANN that uses a supervised learning algorithm. The purpose of this study is to determine the parameters and measure the accuracy of the classification accuracy of the student status of the Open University Statistics Study Program. Based on the results,                  the simulation obtained 15 parameters that can affect student status, including gender, age, education (Senior High School, Diploma, Bachelor, and Magister), marital status, employment status (not working, private employees, entrepreneurs, and civil servants), initial registration year, registration number, semester credit system, and GPA). Meanwhile, for the classification accuracy, the activation function and the learning rate are used minimum mean square of error (MST) on training data. The simulation results are also applied to the testing data with a cut-off point value of 0.3481, so the accuracy of the ROC curve is obtained in the training data for not active students is 99.43% and 99.14% active, while the testing data for not active students is 94.00%. and active 93.94%. So from this research, it can be concluded that ANN Backpropagation is a very good method in applying the classification method.

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Published
2020-06-01
How to Cite
[1]
S. Hasanah and S. Permatasari, “METODE KLASIFIKASI JARINGAN SYARAF TIRUAN BACKPROPAGATION PADA MAHASISWA STATISTIKA UNIVERSITAS TERBUKA”, BAREKENG: J. Math. & App., vol. 14, no. 2, pp. 243-252, Jun. 2020.
Issue
Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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