Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model
Keywords:
Cyclical rule, Forecasting Model, Machine learning, Optimization, Traditional Back-propagation
Abstract
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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References
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[42] M. K. Z. Sormin, P. Sihombing, A. Amalia, A. Wanto, D. Hartama, and D. M. Chan, “Predictions of World Population Life Expectancy Using Cyclical Order Weight / Bias,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.
[43] S. Setti and A. Wanto, “Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World,” JOIN (Jurnal Online Informatika), vol. 3, no. 2, pp. 110–115, 2018.
[44] A. Wanto, S. Defit, and A. P. Windarto, “Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana,” RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, pp. 254–264, 2021
[2] Y. Yin and R. G. Wunderink, “MERS, SARS and other coronaviruses as causes of pneumonia,” Respirology, vol. 23, no. 2, pp. 130–137, 2018.
[3] C. Drosten et al., “Identification of a novel coronavirus in patients with severe acute respiratory syndrome,” New England Journal of Medicine, vol. 348, no. 20, pp. 1967–1976, 2003.
[4] A. M. Zaki, S. Van Boheemen, T. M. Bestebroer, A. D. M. E. Osterhaus, and R. A. M. Fouchier, “Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia,” New England Journal of Medicine, vol. 367, no. 19, pp. 1814–1820, 2012.
[5] S. Zanganeh, N. Goodarzi, M. Doroudian, and E. Movahed, “Potential COVID-19 therapeutic approaches targeting angiotensin-converting enzyme 2; An updated review,” Reviews in Medical Virology, vol. 32, no. 4, pp. 1–14, 2022.
[6] S. Alsammarraie and N. K. Hussein, “A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training,” Tikrit Journal of Pure Science, vol. 25, no. 1, pp. 118–127, 2020.
[7] E. Bas, E. Egrioglu, and U. Yolcu, “A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks,” Journal of Ambient Intelligence and Humanized Computing, pp. 1–9, 2020.
[8] I. T. Sui Kim, V. Sethu, S. K. Arumugasamy, and A. Selvarajoo, “Fenugreek seeds and okra for the treatment of palm oil mill effluent (POME) – Characterization studies and modeling with backpropagation feedforward neural network (BFNN),” Journal of Water Process Engineering, vol. 37, no. 101500, pp. 1–16, 2020.
[9] I. C. Afolabi, S. I. Popoola, and O. S. Bello, “Modeling pseudo-second-order kinetics of orange peel-paracetamol adsorption process using artificial neural network,” Chemometrics and Intelligent Laboratory Systems, vol. 203, no. 104053, pp. 1–47, 2020.
[10] Isha, A. S. Chaudhary, and D. K. Chaturvedi, “Effects of Activation Function and Input Function of ANN for Solar Power Forecasting,” in Lecture Notes in Networks and Systems, vol. 94, M. L. Kolhe, S. Tiwari, M. C. Trivedi, and K. K. Mishra, Eds. Springer, 2020, pp. 329–342.
[11] A. Panyafong, N. Neamsorn, and C. Chaichana, “Heat load estimation using Artificial Neural Network,” Energy Reports, vol. 6, pp. 742–747, 2020.
[12] K. Kumar, V. Singh, and T. Roshni, “Efficacy of hybrid neural networks in statistical downscaling of precipitation of the Bagmati river basin,” Journal of Water and Climate Change, vol. 11, no. 4, pp. 1302–1322, 2020.
[13] M. Žic, V. Subotić, S. Pereverzyev, and I. Fajfar, “Solving CNLS problems using Levenberg-Marquardt algorithm: A new fitting strategy combining limits and a symbolic Jacobian matrix,” Journal of Electroanalytical Chemistry, vol. 866, no. 114171, pp. 1–9, 2020.
[14] J. Bilski, B. Kowalczyk, A. Marchlewska, and J. M. Zurada, “Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks,” Journal of Artificial Intelligence and Soft Computing Research, vol. 10, no. 4, pp. 299–316, 2020.
[15] N. L. W. S. R. Ginantra, M. A. Hanafiah, A. Wanto, R. Winanjaya, and H. Okprana, “Utilization of the Batch Training Method for Predicting Natural Disasters and Their Impacts,” IOP Conf. Series: Materials Science and Engineering, vol. 1071, no. 1, p. 012022, 2021.
[16] R. Jayaseelan, G. Pandulu, and G. Ashwini, “Neural Networks for the Prediction of Fresh Properties and Compressive Strength of Flowable Concrete,” Journal of Urban and Environmental Engineering, vol. 13, no. 1, pp. 183–197, 2019.
[17] H. Espitia, I. Machon, and H. Lopez, “Control of a Microturbine Using Neural Networks,” in Communications in Computer and Information Science, vol. 1052, J. C. Figueroa-García, M. Duarte-González, S. Jaramillo-Isaza, A. D. Orjuela-Cañon, and Y. D.-G. (Eds.), Eds. Springer, 2019, pp. 202–213.
[18] D. Gong, J. Feng, W. Xiao, and S. Sun, “Spectral Reconstruction Based on Bayesian Regulation Neural Network,” in Smart Innovation, Systems and Technologies, vol. 179, R. Kountchev, S. Patnaik, J. Shi, and M. N. Favorskaya, Eds. Springer, 2019, pp. 77–85.
[19] U. G. Inyang, E. E. Akpan, and O. C. Akinyokun, “A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification,” International Journal of Computational Intelligence and Applications, vol. 19, no. 2, pp. 1–20, 2020.
[20] T. Afriliansyah and Z. Zulfahmi, “Prediction of Life Expectancy in Aceh Province by District City Using the Cyclical Order Algorithm,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 268–275, 2020.
[21] G. S. Rao, S. S. Rani, and B. P. Rao, “Computed Tomography Medical Image Compression using Conjugate Gradient,” 2019 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 169–173, 2019.
[22] Q. H. Nguyen et al., “A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns,” Molecules, vol. 25, no. 15, pp. 1–26, 2020.
[23] M. Zandieh, A. Azadeh, B. Hadadi, and M. Saberi, “Application of Artificial Neural Networks for Airline Number of Passenger Estimation in Time Series State,” Journal of Applied Sciences, vol. 9, no. 6, pp. 1001–1013, 2009.
[24] A. Perera, H. Azamathulla, and U. Rathnayake, “Comparison of different artificial neural network ( ANN ) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia,” Journal MAUSAM, vol. 71, no. 2, pp. 233–244, 2020.
[25] C. Perez, Big Data and Deep Learning Examples with Matlab. Lulu Press, Inc, 2020.
[26] P. Parulian et al., “Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters,” in Journal of Physics: Conference Series, 2019, vol. 1255, no. 1.
[27] C. K. Arthur, V. A. Temeng, and Y. Y. Ziggah, “Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction,” Ghana Mining Journal, vol. 20, no. 1, pp. 20–33, 2020.
[28] H. K. Ghritlahre and R. K. Prasad, “Prediction of Thermal Performance of Unidirectional Flow Porous Bed Solar Air Heater with Optimal Training Function Using Artificial Neural Network,” Energy Procedia, vol. 109, pp. 369–376, 2017.
[29] E. Siregar, H. Mawengkang, E. B. Nababan, and A. Wanto, “Analysis of Backpropagation Method with Sigmoid Bipolar and Linear Function in Prediction of Population Growth,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.
[30] M. Tyrtaiou, A. Papaleonidas, A. Elenas, and L. Iliadis, “Accomplished Reliability Level for Seismic Structural Damage Prediction Using Artificial Neural Networks,” in Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, 2020, vol. 2, pp. 85–98.
[31] B. Febriadi, Z. Zamzami, Y. Yunefri, and A. Wanto, “Bipolar function in backpropagation algorithm in predicting Indonesia’s coal exports by major destination countries,” IOP Conference Series: Materials Science and Engineering, vol. 420, no. 1, p. 012087, 2018.
[32] N. Nasution, A. Zamsuri, L. Lisnawita, and A. Wanto, “Polak-Ribiere updates analysis with binary and linear function in determining coffee exports in Indonesia,” IOP Conference Series: Materials Science and Engineering, vol. 420, no. 1, pp. 1–9, 2018.
[33] B. H. Hayadi, I. G. I. Sudipa, and A. P. Windarto, “Model Peramalan Artificial Neural Network pada Peserta KB Aktif Jalur Pemerintahan menggunakan Artificial Neural Network Back-Propagation,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 11–20, 2021.
[34] S. Mohan et al., “An approach to forecast impact of Covid-19 using supervised machine learning model,” Software - Practice and Experience, vol. 52, no. 4, pp. 824–840, 2022.
[35] R. Katoch and A. Sidhu, “An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in India,” Global Business Review, vol. March, pp. 1–14, 2021.
[36] M. Shawaqfah and F. Almomani, “Forecast of the outbreak of COVID-19 using artificial neural network: Case study Qatar, Spain, and Italy,” Results in Physics, vol. 27, no. June, p. 104484, 2021.
[37] Worldometer, “Reported Cases and Deaths by Country or Territory,” COVID-19 Coronavirus Pandemic, 2021. [Online]. Available: https://www.worldometers.info/coronavirus/. [Accessed: 20-Aug-2021].
[38] A. L. Association, “Best Free Reference Web Sites 2011 13th Annual List RUSA Emerging Technologies in Reference Section (MARS),” American Library Association - The Real Time Statistics Project. [Online]. Available: https://www.ala.org/rusa/sections/mars/marspubs/marsbestfreewebsites/marsbestref2011. [Accessed: 05-Jan-2022].
[39] Worldometer, “About Worldometer,” Trusted Authority, 2021. [Online]. Available: https://www.worldometers.info/about/. [Accessed: 05-Jan-2022].
[40] M. O. Shabani and A. Mazahery, “Prediction Performance of Various Numerical Model Training Algorithms in Solidification Process of A356 Matrix Composites,” Indian Journal of Engineering and Materials Sciences, vol. 19, no. 2, pp. 129–134, 2012.
[41] G. W. Bhawika et al., “Implementation of ANN for Predicting the Percentage of Illiteracy in Indonesia by Age Group,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.
[42] M. K. Z. Sormin, P. Sihombing, A. Amalia, A. Wanto, D. Hartama, and D. M. Chan, “Predictions of World Population Life Expectancy Using Cyclical Order Weight / Bias,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–6, 2019.
[43] S. Setti and A. Wanto, “Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World,” JOIN (Jurnal Online Informatika), vol. 3, no. 2, pp. 110–115, 2018.
[44] A. Wanto, S. Defit, and A. P. Windarto, “Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana,” RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, pp. 254–264, 2021
Published
2022-11-30
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
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