Wind Speed Regression Model in Forecasting Wave Height in the Shipping Channel Zone
DOI:
https://doi.org/10.23887/jstundiksha.v12i1.50981Keywords:
Regression Model, Wind Speed, Marine Transport SafetyAbstract
Nowadays, there are often accidents such as the sinking of ships in water areas that are caused by weather factors, one of which is the extreme wind speed. Wind speed and strong waves in the sea are interrelated because the factor that affects the strength of the wave blow is wind speed. This study aims to provide information about the angina speed regression model to find out the wave height at the sea crossing port. This research is a quantitative research. The data used is in the form of wind speed data taken at the coordinates of Lembar Port and Labuan Bajo Port from the NASA sub-agroclimatology website from January 1, 2012, to December 31, 2021 (for 10 years). This case study uses linear regression analysis and is operated using SPSS software. The data shows that in Lembar Port has the most extreme wind speed fluctuations in the 6th and 7th years. While the wind speed fluctuations in Labuan Bajo are consistent, but slightly extreme in the 7th year surpassing the 1st year. The results showed that the R-Square of Lembar port was 15.4% while the R-Square of Labuan Bajo port was 16.2%. The results of this study can be used in formulating marine transportation safety policies in both regions.
References
Abbasi, A., Annor, F. O., & van de Giesen, N. (2016). Investigation of temperature dynamics in small and shallow reservoirs, case study: Lake Binaba, Upper East Region of Ghana. Water (Switzerland), 8(3). https://doi.org/10.3390/w8030084.
Abu Samah, A., Shaffril, H. A. M., Hamzah, A., & Abu Samah, B. (2019). Factors Affecting Small-Scale Fishermen’s Adaptation Toward the Impacts of Climate Change: Reflections From Malaysian Fishers. SAGE Open, 9(3). https://doi.org/10.1177/2158244019864204.
Al Syahrin, M. N. (2018). Kebijakan Poros Maritim Jokowi dan Sinergitas Strategi Ekonomi dan Keamanan Laut Indonesia. Indonesian Perspective, 3(1), 1. https://doi.org/10.14710/ip.v3i1.20175.
Baroya, E. H. (2018). Strategi Pembelajaran Abad 21. As-Salam: Jurnal Ilmiah Ilmu-Ilmu Keislaman, I(01), 101–115.
Brotosusilo, A., Apriana, I. W. A., Satria, A. A., & Jokopitoyo, T. (2016). Littoral and Coastal Management in Supporting Maritime Security for Realizing Indonesia as World Maritime Axis. IOP Conference Series: Earth and Environmental Science, 30(1). https://doi.org/10.1088/1755-1315/30/1/012016.
De Giorgi, M. G., Congedo, P. M., & Malvoni, M. (2014). Photovoltaic power forecasting using statistical methods: Impact of weather data. IET Science, Measurement and Technology, 8(3), 90–97. https://doi.org/10.1049/iet-smt.2013.0135.
Fikri, A. (2013). Penerapan Data Mining Untuk Mengetahui Tingkat Kekuatan Beton Yang Dihasilkan Dengan Metode Estimasi Menggunakan Linear Regression. Fakultas Ilmu Komputer UDINUS, 1–12.
Hendra Jaya, Rudi Gunawan, R. K. (2019). Model Peramalan Konsumsi Bahan Bakar Jenis Premium Di Indonesia Dengan Regresi Linier Berganda. Jurnal Ilmiah Teknik Industri, 227(2), 227.
Jamdade, S. G., & Jamdade, P. G. (2012). Analysis of wind speed data for four locations in ireland based on weibull distribution’s linear regression model. International Journal of Renewable Energy Research, 2(3), 451–455.
Jhonson Arizona Saragih, I., Rumahorbo, I., Yudistira, R., Dedi Sucahyono, dan, Meteorologi Klimatologi dan Geofisika, B., Meteorologi Kualanamu, S., … Selatan, T. (2020). Prediksi Curah Hujan Bulanan Di Deli Serdang Menggunakan Persamaan Regresi Dengan Prediktor Data Suhu Dan Kelembapan Udara. Jurnal Meteorologi Klimatologi Dan Geofisika, 7(2), 6–14.
Liu, Y., Zhang, S., Chen, X., & Wang, J. (2018). Artificial combined model based on hybrid nonlinear neural network models and statistics linear models-research and application for wind speed forecasting. Sustainability (Switzerland), 10(12). https://doi.org/10.3390/su10124601.
Lowry P B, & J, G. (2014). Partial Least Squares (PLS) Structural Equation Modeling (SEM) for Building and Testing Behavioral Causal Theory: When to Choose It and How to Use It. IEEE Transactions on Professional Communication, 57(2), 123–146.
Luthfiarta, A., Febriyanto, A., Lestiawan, H., & Wicaksono, W. (2020). Analisa Prakiraan Cuaca dengan Parameter Suhu, Kelembaban, Tekanan Udara, dan Kecepatan Angin Menggunakan Regresi Linear Berganda. JOINS (Journal of Information System), 5(1), 10–17. https://doi.org/10.33633/joins.v5i1.2760.
Marino, E., Giusti, A., & Manuel, L. (2017). Offshore wind turbine fatigue loads: The influence of alternative wave modeling for different turbulent and mean winds. Renewable Energy, 102, 157–169. https://doi.org/10.1016/j.renene.2016.10.023.
Nababan, B., Rosyadi, N., Manurung, D., Natih, N. M., & Hakim, R. (2016). The Seasonal Variability of Sea Surface Temperature and Chlorophyll-a Concentration in the South of Makassar Strait. Procedia Environmental Sciences, 33, 583–599. https://doi.org/10.1016/j.proenv.2016.03.112.
Nalina, U., Prema, V., Smitha, K., & Rao, K. U. (2014). Multivariate regression for prediction of solar irradiance. Proceedings - 2014 International Conference on Data Science and Engineering, ICDSE 2014, 177–181. https://doi.org/10.1109/ICDSE.2014.6974633.
Ngurah, I. G., Yogiswara, A., & Sutrisna, I. K. (2021). Pengaruh Perubahan Iklim Terhadap Hasil Produksi Ikan di Kabupaten Badung. E-Jurnal EP Unud, 10(9), 3613–3643. https://doi.org/ojs.unud.ac.id.
Permai, S. D., & Tanty, H. (2018). Linear regression model using bayesian approach for energy performance of residential building. Procedia Computer Science, 135, 671–677. https://doi.org/10.1016/j.procs.2018.08.219.
Purwendah, E. K. (2020). Sea Protection From Oil Pollution. Ganesha Law Review, 2(1), 77–89.
Putri, A., Syafrialdi, Y., & Mustakim, M. (2017). Analisa Pengaruh Temperatur Terhadap Titik Embun, Jarak Pandang, Kecepatan Angin, Dan Curah Hujan Metode Regresi Linier Berganda. Seminar Nasional Teknologi Informasi Komunikasi Dan Industri, 227–234.
Rahmaniar, J., Arsyad, M., & Tiwow, V. A. (2020). Pengaruh Madden Julian Oscillation ( MJO ) terhadap Tinggi Gelombang Laut di Selat Makassar. Seminar Nasional Fisika 2020 Program, 3(pp), 52–55.
Riyandiarto, B. B., & Fadjrin, N. N. (2020). Pengembangan Aplikasi Regresi Parameter Cuaca Berbasis Visual Basic For Application. Jurnal Teknik Informatika Dan Sistem Informasi, 6(3), 549–563. https://doi.org/10.28932/jutisi.v6i3.3038.
Rodrigue, J.-P., & Notteboom, T. (2012). The Geography of Cruise Shipping : Itineraries , Capacity Deployment and Ports of Call. ALRT 2012 Conference - Vancouver, 38, 6–8.
Saigal, S., & Mehrotra, D. (2012). Performance Comparison Of Time Series Data Using Predictive Data Mining Techniques. Advances in Information Mining, 4(1), 57–66.
Saragih, E. J., Mulyono, J., & Santoso, H. (2018). Desain baling-baling kincir angin sumbu horizontal. Scientific Journal Widya Teknik, 17(2), 63–71.
Sari, A. Q., Sukestiyarno, Y. L., & Agoestanto, A. (2017). Batasan Prasyarat Uji Normalitas Dan Uji Homogenitas Pada Model Regresi Linear. Unnes Journal of Mathematics, 6(2), 168–177.
Sari, V., & Maulidany, D. A. (2020). Gelombang Air Laut Terhadap Skala Beaufort Dengan Metode Hybrid Arima-Ann. Statistika, 8(1).
Sarwat, A. I., Amini, M., Domijan, A., Damnjanovic, A., & Kaleem, F. (2016). Weather-based interruption prediction in the smart grid utilizing chronological data. Journal of Modern Power Systems and Clean Energy, 4(2), 308–315. https://doi.org/10.1007/s40565-015-0120-4.
Stanev, E. V., Peneva, E., & Chtirkova, B. (2019). Climate Change and Regional Ocean Water Mass Disappearance: Case of the Black Sea. Journal of Geophysical Research: Oceans, 124(7), 4803–4819. https://doi.org/10.1029/2019JC015076.
Stathopoulos, T., Zisis, I., & Xypnitou, E. (2014). Local and overall wind pressure and force coefficients for solar panels. Journal of Wind Engineering and Industrial Aerodynamics, 125, 195–206. https://doi.org/10.1016/j.jweia.2013.12.007.
Sulistyono, S., & Sulistiyowati, W. (2017). Peramalan Produksi dengan Metode Regresi Linier Berganda. PROZIMA (Productivity, Optimization and Manufacturing System Engineering), 1(2), 82–89. https://doi.org/10.21070/prozima.v1i2.1350.
Williams, M., Gomez Grajales, C. A., & Kurkiewicz, D. (2013). Assumptions of Multiple Regression: Correcting Two Misconceptions - Practical Assessment, Research & Evaluation. Evaluación Práctica, Investigación y Evaluación, 18(11), 1–16.
Yang, F. B., Xue, X. Y., Zhang, L., & Sun, Z. (2017). Numerical simulation and experimental verification on downwash air flow of six-rotor agricultural unmanned aerial vehicle in hover. International Journal of Agricultural and Biological Engineering, 10(4), 41–53. https://doi.org/10.25165/j.ijabe.20171004.3077.
Young, I. R., & Ribal, A. (2019). Multiplatform evaluation of global trends in wind speed and wave height. Science, 364(6440), 548–552. https://doi.org/10.1126/science.aav9527.
Zhang, Shi, Solari, G., Yang, Q., & Repetto, M. P. (2018). Extreme wind speed distribution in a mixed wind climate. Journal of Wind Engineering and Industrial Aerodynamics, 176, 239–253. https://doi.org/10.1016/j.jweia.2018.03.019.
Zhang, Shuanghong, Chen, J., Wan, Z., Yu, M., Shu, Y., Tan, Z., & Liu, J. (2021). Challenges and countermeasures for international ship waste management: IMO, China, United States, and EU. Ocean and Coastal Management, 213. https://doi.org/10.1016/j.ocecoaman.2021.105836.
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