PEMANFAATAN JARINGAN SARAF TIRUAN PROPAGASI BALIK UNTUK MODEL PREDIKSI DERET WAKTU PASANG SURUT

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Afif Widaryanto
Fineza Ilova

Abstract

[The Use of Backpropagation Neural Network for Time Series Tide-Level Prediction Model] Most of ocean related activities require tidal prediction data. This requires a prediction system with high accuracy. The widespread application of artificial intelligence with its various reliability inspired this research to apply tidal prediction models using artificial neural networks. With the input of tidal data for the previous seven days to predict the tide for the next 6 and 12 hours, it can be modeled using an artificial network based on back propagation learning method. As a result, the performance of the prediction model testing is very satisfying with an average accuracy of above 90% and low MSSE (mean sum square error) values.
Keywords: tide; artificial intelligent; neural network; prediction model

Article Details

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Author Biographies

Afif Widaryanto, Badan Pengkajian dan Penerapan Teknologi

Balai Teknologi Survei Kelautan

Fineza Ilova, Badan Pengkajian dan Penerapan Teknologi

Balai Teknologi Survei Kelautan

References

B.L. Meenaa , Dr. J.D. Agrawalb. (2015) ‘Tidal Level Forcasting Using ANN’, 8th International Conference on Asian and Pacific Coasts 2015, doi: 10.1016/j.proeng.2015.08.332

C hakraborty K ., Melhotra K ., Mohan C .K ., and R anka S . (1992) ‘Forecasting the behaviour of multivariate time series using neural network’, Neural Networks 5(6)961-970.

Rasel, Uddin, and Haroon. (2018) ‘Application of DNN for Predicting River Tide Level’, 2nd Int. Conf. on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh

S. Hayman, "The McCulloch-Pitts model," in IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999, vol. 6, pp. 4438-4439 vol.6.

Wang, Yuan, and Tan.(2015) ‘Application of BP Neural Network in Monitoring of Ocean Tide Level’, International Conference on Computational Intelligence and Communication Networks 2015, doi:10.1109/CICN.2015.238

Widaryanto and Kusumoputro, "Modeling and Designing Direct Inverse Control Using Back-propagation Neural Network for Skid Steering Boat Model," 2019 IEEE International Conference on Innovative Research and Development (ICIRD), Jakarta, Indonesia, 2019, pp. 1-5, doi: 10.1109/ICIRD47319.2019.9074761.

Winter and Widrow, "MADALINE RULE II: a training algorithm for neural networks," in IEEE 1988 International Conference on Neural Networks, 1988, pp. 401-408 vol.1.

Yen, P-H, Jan, C-D, Lee, Y-P and Lee, H-F (1996) Application of Kalman filter to short-term tide level prediction, .J. Waterway, Port, Coastal and Ocean Engineering, ASCE, 122(5), pp 226- 231.