Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model

  • Anjar Wanto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia http://orcid.org/0000-0003-4891-084X
  • Ni Luh Wiwik Sri Rahayu Ginantra Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
  • Surya Hendraputra Politeknik Ganesha, Medan, Indonesia
  • Ika Okta Kirana STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Abdi Rahim Damanik STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Keywords: Cyclical rule, Forecasting Model, Machine learning, Optimization, Traditional Back-propagation


The traditional Back-propagation algorithm has several weaknesses, including long training times and significant iterations to achieve convergence. This study aims to optimize traditional Back-propagation using the cyclical rule method to cover these weaknesses. Optimization is done by changing the training function and standard Back-propagation parameters using the training function and cyclical rule parameters. After that, a comparison of the two results will be carried out. This study uses quantitative method of time-series data on coronavirus cases sourced from the Worldometer website, then analyzed using three forecasting models with five input layers, one hidden layer (5, 10, and 15 neurons) and one output layer. The results showed that the 5-10-1 model with the training function and cyclical rule parameters and the tansig and purelin activation functions could perform well in optimization, including faster training time and smaller iterations (epochs), MSE training performance, and better tests. Low and high accuracy (92%) with an error rate of 0.01. So it was concluded that the training function and cyclical rule parameters with the tansig and purelin activation functions were able to optimize the traditional Back-propagation method, and the 5-10-1 model could be used for forecasting active cases of the coronavirus in Asia


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How to Cite
Wanto, A., Ginantra, N. L. W. S. R., Hendraputra, S., Kirana, I. O., & Damanik, A. R. (2022). Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(1), 51-82. https://doi.org/https://doi.org/10.30812/matrik.v22i1.1826