LONG SHORT-TERM MEMORY FOR PREDICTION OF WAVE HEIGHT AND WIND SPEED USING PROPHET FOR OUTLIERS

Authors

  • Galih Restu Baihaqi Trunojoyo Madura University, Indonesia
  • Mulaab Trunojoyo Madura University, Indonesia

DOI:

https://doi.org/10.21107/kursor.v12i2.351

Keywords:

LSTM, Prophet, Newton Interpolation, Wave Height, Wind Speed

Abstract

The reason fishermen lose control is wave height and wind speed. The impact is also felt by all users of the marine sector. This research uses the Long Short Term Memory (LSTM) method because this method has accurate values in the forecasting process with a lot of historical data and uses the Prophet method to detect outliers with Newton interpolation to replace the detected outlier data. The total number of data was 2074 obtained from BMKG Perak Surabaya from January 2020 to November 2022 at four research points, namely north, northeast, east and south points. The test results provide varying error values with MAPE as the model evaluation value. The error value for sea wave height at the north, northeast, east and south points is 13.32 respectively; 13.32; 9.32 and 8.85 with data without interpolation. Meanwhile, the error value in the wind speed data is 14.74; 14.85; 15.14 and 14.52 with a 3rd order Newton interpolation process at the northeast and east points. MAPE values below 20% prove that the LSTM model is good for predicting wave height and wind speed data at four points in Sumenep Regency. The system implementation is made into a web-based application.

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References

D. Vinezzia, “Identifikasi Bahaya Keselamatan dan Kesehatan Kerja pada Aktivitas Nelayan,” JPPP, vol. III, no. 1, pp. 117–126, 2021 [Online]. Available: http://jurnal.globalhealthsciencegroup.com/index.php/JPPP

A. Harahap, W. Khalfianur, and C. Riska Niati, “Pengaruh Gelombang Laut Terhadap Hasil Tangkapan Nelayan di Kuala Langsa,”Samudra Akuatika, vol. I, no. 2, pp. 21–25, Nov. 2017.

S. U. Utami, E. K. S. Harini Muntasib, D. Agustinus, and M. Samosir, “Manajemen Bahaya di Kawasan Wisata Manajemen Bahaya di Kawasan Wisata Pantai Karang Hawu, Kabupaten Sukabumi, Jawa Barat,” Media Konservasi, vol. XIV, no. 3, pp. 322–333, Dec. 2019.

V. Juliani, D. Adytia, and Adiwijaya, “Wave Height Prediction based on Wind Information by using General Regression Neural Network, study case in Jakarta Bay,” in 2020 8th International Conference on Information and Communication Technology (ICoICT), IEEE, Jun. 2020, pp. 1–5. doi: 10.1109/ICoICT49345.2020.9166305.

E. Supriyadi, “Prediksi Parameter Cuaca Menggunakan Deep Learning Long-Short Term Memory (LSTM),” Meteorologi dan Geofisika, vol. XXI, no. 2, pp. 55–67, 2020, [Online]. Available: http://bmkgsoft.database.bmkg.go.id.

S. Sen, D. Sugiarto, and A. Rochman, “Komparasi Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM) dalam Peramalan Harga Beras,” ULTIMATICS, vol. XII, no. 1, pp. 35–41, 2020.

A. Haghshenas, A. Hasan, O. Osen, and E. T. Mikalsen, “Predictive digital twin for offshore wind farms,” Energy Informatics, vol. VI, no. 1, Dec. 2023, doi: 10.1186/s42162-023-00257-4.

B. A. Aprian, Y. Azhar, V. Rahmayanti, and S. Nastiti, “Prediksi Pendapatan Kargo Menggunakan Arsitektur Long Short Term Memory,” Jurnal Komputer Terapan, vol. VI, no. 2, pp. 148–157, 2020, [Online]. Available: https://jurnal.pcr.ac.id/index.php/jkt/

A. Hammaines, C. Setianingsih, and M. A. Murti, “Prediksi Penggunaan Energi Listrik Menggunakan Metode Feedforward Neural Network,” in eProceeding of Engineering, Bandung, Dec. 2021, pp. 12125–12134.

Y. A. Venanda, “Hubungan efikasi diri (self efficacy) dengan prokrastinasi akademik dalam penyelesaian skripsi pada mahasiswa,” Jurnal Psikologi Tabularasa, vol. XVII, no. 1, pp. 40–55, Aug. 2022, doi: 10.26905/jpt.v17i1.8090.

M. Ignasius and J. Lamabelawa, “Perbandingan Interpolasi dan Ekstrapolasi Newton untuk Prediksi Data Time Series,” HOAQ, vol. X, no. 2, pp. 974–982, 2019.

K. Thiyagarajan, S. Kodagoda, N. Ulapane, and M. Prasad, “A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System,” in Conference on Industrial Electronics and Applications (ICIEA), 2020.

X. Chu, W. Cui, S. Xu, L. Zhao, H. Guan, and Y. Ge, “Multiscale Time Series Decomposition for Structural Dynamic Properties: Long Term Trend and Ambient Interference,” Struct Control Health Monit, pp. 1–18, Feb. 2023, doi: 10.1155/2023/6485040.

A. N. Asvikarani, I. Made Widiartha, and M. A. Raharja, “Foreign Tourist Arrival Forecasting To Bali Using Cascade Forward Backpropagation,” KURSOR, vol. X, no. 4, pp. 145–152, 2020.

S. Ilma, N. Suwandi, R. Tyasnurita, and H. Muhayat, “Peramalan Emisi Karbon Menggunakan Metode SARIMA dan LSTM,” J-COSINE, vol. VI, no. 1, pp. 73–80, 2022, [Online]. Available: http://jcosine.if.unram.ac.id/

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Published

2023-12-10

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