Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana

Keywords: Optimization, Training Functions, Machine Learning, Forecasting, Natural Disasters

Abstract

Research has been carried out with several training functions using standard backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose of this study was to (i) analyze the Performance accuracy (Performance) of the standard backpropagation method and (ii) optimize the training function with the One-Step Secant (OSS) and Bayesian regulation methods to obtain comparison results of the three methods in the search for the best results implementation of disaster phenomenon forecasting data. The research method is based on quantitative methods with times-series data on disaster phenomena in Indonesia over the last ten years (2011-2020) which were analyzed using two network architecture models, namely 4-8-1 and 4-10-1. The results showed that the 4-8-1 architectural model with the Bayesian regulation training function method was able to optimize quite well through accelerating training time and resulted in a low MSE measurement, although not the lowest with an epoch value of 197 iterations and a Performance of 0.0148480766. The lowest epoch value is generated by the OSS method, but it Performs poorly. The best Performance is produced by the standard backpropagation method with the traingd training function, but the training process for achieving convergence is also too long. In general, it can be concluded that the 4-8-1 architectural model with Bayesian regulation can be used to predict (predict) the phenomenon of natural disasters in Indonesia because the training time to achieve convergence is not too long and Performs exceptionally well.

 

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References

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.

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, p. 012023, 2019.

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.

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.

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.

A. Dolara, F. Grimaccia, S. Leva, M. Mussetta, and E. Ogliari, “Comparison of training approaches for photovoltaic forecasts by means of machine learning,” Applied Sciences (Switzerland), vol. 8, no. 2, 2018.

H. Wang, R. Czerminski, and A. C. Jamieson, “Neural Networks and Deep Learning,” in The Machine Age of Customer Insight, P. Einhorn, M., Löffler, M., de Bellis, E., Herrmann, A. and Burghartz, Ed. Emerald Publishing Limited, 2021, pp. 91–101.

I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” Nature Physics, vol. 15, no. 12, pp. 1273–1278, 2019.

B. S. Rem et al., “Identifying quantum phase transitions using artificial neural networks on experimental data,” Nature Physics, vol. 15, no. 9, pp. 917–920, 2019.

R. Novickis, D. J. Justs, K. Ozols, and M. Greitans, “An Approach of Feed-Forward Neural Network,” Electronics, vol. 9, no. 12, p. 2193, 2020.

F. Cichos, K. Gustavsson, B. Mehlig, and G. Volpe, “Machine learning for active matter,” Nature Machine Intelligence, vol. 2, no. 2, pp. 94–103, 2020.

C. T. Chen and G. X. Gu, “Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning,” Advanced Science, vol. 7, no. 5, pp. 1–10, 2020.

R. García-Ródenas, L. J. Linares, and J. A. López-Gómez, “Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm,” Neural Computing and Applications, vol. 33, pp. 2561–2588, 2020.

E. Yan, J. Song, C. Liu, J. Luan, and W. Hong, “Comparison of support vector machine, back propagation neural network and extreme learning machine for syndrome element differentiation,” Artificial Intelligence Review, vol. 53, no. 4, pp. 2453–2481, 2020.

L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020.

I. D. Uwanuakwa and P. Akpinar, “Investigations on the Influence of Variations in Hidden Neurons and Training Data Percentage on the Efficiency of Concrete Carbonation Depth Prediction with ANN,” Advances in Intelligent Systems and Computing, vol. 1095, pp. 958–965, 2020.

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.

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.

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.

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.

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.

A. Panyafong, N. Neamsorn, and C. Chaichana, “Heat load estimation using Artificial Neural Network,” Energy Reports, vol. 6, pp. 742–747, 2020.

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.

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.

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.

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. 012022, pp. 1–7, 2021.

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.

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.

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.

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.

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.

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.

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.

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.

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.

C. Perez, Big Data and Deep Learning Examples with Matlab. Lulu Press, Inc, 2020.

P. Parulian et al., “Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters,” Journal of Physics: Conference Series, vol. 1255, no. 1, p. 012033, 2019.

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.

BNPB, “Infografis Bencana,” Geoportal Kebencanaan Indonesia, 2021. [Online]. Available: https://gis.bnpb.go.id/. [Accessed: 17-Mar-2021].

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.

Published
2021-04-28
How to Cite
Wanto, A., Defit, S., & Perdana Windarto, A. (2021). Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 254 - 264. https://doi.org/10.29207/resti.v5i2.3031
Section
Artikel Rekayasa Sistem Informasi