BACKPROPAGATION ARTIFICIAL NEURAL NETWORKS PREDICTION OF ELEMENTARY SCHOOL STUDENT GRADUATION WITH EXAMINATION PRACTICE SCORE

Authors

  • Azzahra Rahmawati Sunaryo Universitas PGRI Yogyakarta
  • Tri Hastono Universitas PGRI Yogyakarta
  • Nevanda Abelia Universitas PGRI Yogyakarta

DOI:

https://doi.org/10.61677/jth.vi.4

Keywords:

Prediksi Kelulusan, Backpropagation, Nilai Latih Ujian

Abstract

Backpropagation method is a computer technique to help predict and sort data. This method is usually used to change the connection between parts of the computer's brain in the hidden layer. Meanwhile, the Nervous System Network (ANN) is an information processing system that is very similar to the function of human brain cells. Value is a benchmark for a student's graduation, if the student'sscore is getting better, the more opportunities for the student's graduation. In predicting this pass using the method of Artificial Neural Networks (ANN), namely Backpropagation and using Matlab software with the MSE (Mean Square Error) result of 0.099512.

Keywords: Graduation Prediction, Backpropagation, Examination Practice Value, Artificial Neural Networks, Matlab

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Published

2023-07-31 — Updated on 2023-10-12

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How to Cite

Sunaryo, A. R., Hastono, T., & Abelia, N. (2023). BACKPROPAGATION ARTIFICIAL NEURAL NETWORKS PREDICTION OF ELEMENTARY SCHOOL STUDENT GRADUATION WITH EXAMINATION PRACTICE SCORE. JTH: Journal of Technology and Health, 1(1), 10–20. https://doi.org/10.61677/jth.vi.4 (Original work published July 31, 2023)

Issue

Vol. 1 No. 1 (2023): July: JTH: Journal of Technology and Health

Section

Articles