Prediction of Extreme Sea Water Waves at Ancol Beach Using ID3 Algoritma Algorithm
Article Sidebar
Published:
Jun 30, 2022
Section
Articles
Statistics Article
Article View : 263 Times
Main Article Content
Abstract
Ocean waves are natural events where water waves gradually move up and down. This regular rise and fall of water is one of the important aspects of transportation, predicting potential trade disasters and tsunamis in water areas. Know the data The future beyond the level of ocean waves can bring great benefits smoothly Transport and trade of territorial waters. Future data can be obtained from forecasts with certain algorithms. The ID3 algorithm is one of the most common learning algorithms. Used to create a decision tree or decision tree. The result of this analysis is a decision tree that can be used for classifying sea level using an accuracy of 88%.
Downloads
Download data is not yet available.
Article Details
How to Cite
Budi Harti, A., Dionysius Hendard Christianto, Nabillah, R. ., & Oktavia, . M. . (2022). Prediction of Extreme Sea Water Waves at Ancol Beach Using ID3 Algoritma Algorithm. Journal of Intelligent Decision Support System (IDSS), 5(2), 64-72. https://doi.org/10.35335/idss.v5i2.80
References
[1] D. Fitriana, N. Oktaviani, and I. U. Khasanah, “Analisa harmonik pasang surut dengan metode admiralty pada stasiun berjarak kurang dari 50 km,” J. Meteorol. Klimatologi dan Geofis., vol. 6, no. 1, pp. 38–48, 2019.
[2] E. Supriyadi and W. S. Siswanto, “Analisis Pasang Surut di Perairan Pameungpeuk, Belitung, dan Sarmi Berdasarkan Metode Admiralty,” J. Meteorol. dan Geofis., vol. 19, no. 1, pp. 29–38, 2019.
[3] D. Sufianto, “Pasang surut otonomi daerah di Indonesia,” J. Acad. Praja, vol. 3, no. 02, pp. 271–288, 2020.
[4] J. Risandi and D. Candra, “Prediksi Gelombang Ekstrim di Kepulauan Seribu untuk Aplikasi Kelautan dan Perikanan,” J. Ris. Jakarta, vol. 14, no. 2, pp. 51–56, 2021.
[5] H. R. Maulana, “Analisis Karakteristik Gelombang Laut Dan Prediksi Gelombang Ekstrim Dengan Periode Ulang Terkait Keselamataan Pelayaran Di Perairan Utara Jakarta.” Universitas Brawijaya, 2018.
[6] J. V. Reonaldho, D. Saepudin, and D. Adytia, “Prediksi Gelombang Ekstrim Air Laut Di Pelabuhan Tanjung Priok Menggunakan Algoritma Id3,” eProceedings Eng., vol. 7, no. 1, 2020.
[7] S. U. Anggono, D. Manongga, and A. Iriani, “Evaluasi Pelayanan Terpadu Satu Pintu (PTSP) Menggunakan Decision Tree,” J. MEDIA Inform. BUDIDARMA, vol. 5, no. 4, pp. 1208–1216, 2021.
[8] M. Jeihouni, A. Toomanian, and A. Mansourian, “Decision tree-based data mining and rule induction for identifying high quality groundwater zones to water supply management: a novel hybrid use of data mining and GIS,” Water Resour. Manag., vol. 34, no. 1, pp. 139–154, 2020.
[9] H. Rodrigo, E. W. Beukes, G. Andersson, and V. Manchaiah, “Exploratory data mining techniques (decision tree models) for examining the impact of internet-based cognitive behavioral therapy for tinnitus: Machine learning approach,” J. Med. Internet Res., vol. 23, no. 11, p. e28999, 2021.
[10] H. H. Patel and P. Prajapati, “Study and analysis of decision tree based classification algorithms,” Int. J. Comput. Sci. Eng., vol. 6, no. 10, pp. 74–78, 2018.
[2] E. Supriyadi and W. S. Siswanto, “Analisis Pasang Surut di Perairan Pameungpeuk, Belitung, dan Sarmi Berdasarkan Metode Admiralty,” J. Meteorol. dan Geofis., vol. 19, no. 1, pp. 29–38, 2019.
[3] D. Sufianto, “Pasang surut otonomi daerah di Indonesia,” J. Acad. Praja, vol. 3, no. 02, pp. 271–288, 2020.
[4] J. Risandi and D. Candra, “Prediksi Gelombang Ekstrim di Kepulauan Seribu untuk Aplikasi Kelautan dan Perikanan,” J. Ris. Jakarta, vol. 14, no. 2, pp. 51–56, 2021.
[5] H. R. Maulana, “Analisis Karakteristik Gelombang Laut Dan Prediksi Gelombang Ekstrim Dengan Periode Ulang Terkait Keselamataan Pelayaran Di Perairan Utara Jakarta.” Universitas Brawijaya, 2018.
[6] J. V. Reonaldho, D. Saepudin, and D. Adytia, “Prediksi Gelombang Ekstrim Air Laut Di Pelabuhan Tanjung Priok Menggunakan Algoritma Id3,” eProceedings Eng., vol. 7, no. 1, 2020.
[7] S. U. Anggono, D. Manongga, and A. Iriani, “Evaluasi Pelayanan Terpadu Satu Pintu (PTSP) Menggunakan Decision Tree,” J. MEDIA Inform. BUDIDARMA, vol. 5, no. 4, pp. 1208–1216, 2021.
[8] M. Jeihouni, A. Toomanian, and A. Mansourian, “Decision tree-based data mining and rule induction for identifying high quality groundwater zones to water supply management: a novel hybrid use of data mining and GIS,” Water Resour. Manag., vol. 34, no. 1, pp. 139–154, 2020.
[9] H. Rodrigo, E. W. Beukes, G. Andersson, and V. Manchaiah, “Exploratory data mining techniques (decision tree models) for examining the impact of internet-based cognitive behavioral therapy for tinnitus: Machine learning approach,” J. Med. Internet Res., vol. 23, no. 11, p. e28999, 2021.
[10] H. H. Patel and P. Prajapati, “Study and analysis of decision tree based classification algorithms,” Int. J. Comput. Sci. Eng., vol. 6, no. 10, pp. 74–78, 2018.