Implementation of singular spectrum analysis in the forecasting of seawater wave height

Wika Dianita Utami, Ade Candra Agustina

Abstract


Indonesia is renowned as a maritime nation, positioned amidst the Pacific Ocean and the Indian Ocean. This strategic location grants Indonesia the distinct advantage of serving as a global crossroads for maritime traffic, particularly with regards to trade and waterborne transportation. Among Indonesia's bustling ports, Tanjung Priok Port stands out as one of the busiest. In this context, the measurement of seawater wave height assumes a pivotal role in shaping the dynamics of transportation and commercial activities at Tanjung Priok Port. Hence, the availability of predictive insights into forthcoming seawater wave height assumes paramount significance in proactively addressing potential calamities and orchestrating maritime endeavors more efficaciously. This study aims to apply the Singular Spectrum Analysis (SSA) technique to forecast the wave height of seawater at Tanjung Priok Port. The dataset employed encompasses the daily seawater wave height observations recorded at Tanjung Priok Harbor during the timeframe from January 2022 to May 2023. The findings of this research unveil a parameter value of L = 98, a Grouping Effect (r) of 13, and a Mean Absolute Percentage Error (MAPE) value of 10.01%. This MAPE value signifies that the forecasting yielded by the Singular Spectrum Analysis (SSA) methodology exhibits a satisfactory level of accuracy in prognosticating future seawater wave heights at Tanjung Priok Port.


Keywords


forecasting; seawater wave height; singular spectrum analysis

Full Text:

PDF

References


Asrof, A. (2017). Peramalan produksi cabai merah di jawa barat menggunakan metode singular spectrum analysis (ssa). STATISTIKA: Journal of Theoretical Statistics and Its Applications, 17(2), 77–87. https://doi.org/10.29313/jstat.v17i2.2839

Broomhead, D. S., & King, G. P. (1986). Extracting qualitative dynamics from experimental data. Physica D: Nonlinear Phenomena, 20(2–3), 217–236. https://doi.org/10.1016/0167-2789(86)90031-X

Dewi, Z. Z. R. (2021). Prediksi tinggi gelombang di pelabuhan ketapang menggunakan model fungsi transfer. UIN Sunan Ampel Surabaya, Surabaya.

Ete, A. A. (2017). Forecasting the amount of foreign tourist travelers to indonesia by entrance guide using singular spectrum analysis and arima. Institut Teknologi Sepuluh Nopember, Surabaya.

Fitri, F., Gamayanti, N. F., & Gunawan, G. (2017). Metode ssa pada data produksi perikanan tangkap di provinsi jawa barat. Jurnal Ilmiah Matematika Dan Pendidikan Matematika, 9(2), 95. https://doi.org/10.20884/1.jmp.2017.9.2.2870

Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. A. (2001). Analysis of time series structure. In Analysis of Time Series Structure. https://doi.org/10.1201/9780367801687

Irwan, Adnan Sauddin, & Anita Kaimuddin. (2022). Proyeksi produksi padi kabupaten pinrang dengan metode singular spectrum analysis. Jurnal MSA (Matematika Dan Statistika Serta Aplikasinya), 10(1). https://doi.org/10.24252/msa.v10i1.29869

Jatmiko, Y. A., Rahayu, R. L., & Darmawan, G. (2017). Perbandingan keakuratan hasil peramalan produksi bawang merah metode holt-winters dengan singular spectrum analysis (ssa). Jurnal Matematika “MANTIK,” 3(1), 13. https://doi.org/10.15642/mantik.2017.3.1.13-24

Koad, P., & Jaroensutasinee, K. (2021). Application of Singular Spectrum Analysis in Deep-Ocean Tide Reconstruction and Prediction. Walailak Journal of Science and Technology (WJST), 18(3), 1–21. https://doi.org/10.48048/wjst.2020.7115

Krishnannair, S., & Aldrich, C. (2019). Process monitoring and fault detection using empirical mode decomposition and singular spectrum analysis. IFAC-PapersOnLine, 52(14), 219–224. https://doi.org/10.1016/j.ifacol.2019.09.190

Loizou, A., & Christmas, J. (2022). Sea state from ocean video with singular spectrum analysis and extended kalman filter. Signal, Image and Video Processing, 16(6), 1497–1504. https://doi.org/10.1007/s11760-021-02103-0

Lubis, D. A., Johra, M. B., & Darmawan, G. (2017). Peramalan indeks harga konsumen dengan metode singular spectral analysis (ssa) dan seasonal autoregressive integrated moving average (sarima). Jurnal Matematika “MANTIK,” 3(2), 74–82. https://doi.org/10.15642/mantik.2017.3.2.74-82

Rahmadani, N., Setiawan, B. D., & Adinugroho, S. (2019). Prediksi ketinggian gelombang laut menggunakan metode jaringan saraf tiruan backpropagation. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(7), 6517–6525.

Reonaldho, J. V., Saepudin, D., & Adytia, D. (2020). Prediksi gelombang ekstrim air laut di pelabuhan tanjung priok menggunakan algoritma id3. EProceedings of Engineering, 7(1), 2700–2713.

Sodiqin, M. A., Sulandari, W., & Respatiwulan. (2021). The application of singular spectrum analysis method in forecasting the number of foreign tourists visit to special capital region of jakarta. Jurnal Riset Dan Aplikasi Matematika (JRAM), 5(2), 92–102.

Sulandari, W., Subanar, S., Lee, M. H., & Rodrigues, P. C. (2020). Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks. MethodsX, 7, 101015. https://doi.org/10.1016/j.mex.2020.101015

Swart, S. B., Otter, A. R. den, & Lamoth, C. J. C. (2022). Singular spectrum analysis as a data-driven approach to the analysis of motor adaptation time series. Biomedical Signal Processing and Control, 71, 103068. https://doi.org/10.1016/j.bspc.2021.103068




DOI: http://dx.doi.org/10.24042/djm.v6i3.18382

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Desimal: Jurnal Matematika

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

  Creative Commons License
Desimal: Jurnal Matematika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.