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SINTA 1
Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2024-06-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
The sea brings many benefits for society, especially for a maritime country such as Indonesia. The potential in various sectors is limited only by the willingness of a party to invest in it. One such investment is in learning the knowledge and information that can be gathered from the sea, and even predicting its behavior with enough data. Using a residual LSTM algorithm, we will predict the tidal level in eastern Bintan island, a tropical island on the tip of Malay peninsula. The dataset is acquired from two sensor points in eastern Bintan coast from July 2018 to June 2019 for a span of one year, giving a total of 7,961 data points. The residual LSTM model consists of a residual wrapper with two consecutive LSTM layers and one dense layer. The model is also compared with variations of LSTM and RNN models. The result of the residual LSTM model has an MAE value of 0.1495 cm and an RMSE value of 0.3353 cm, compared to the baseline model’s 1.1148 cm and 1.4107 cm respectively. The model also has an RMSE value improvement of 76.23% compared to the base model.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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<dc:title xml:lang="en-US">Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory</dc:title>
<dc:creator>Syakti, Agsanshina Raka</dc:creator>
<dc:creator>Rhamadhan, Syahri</dc:creator>
<dc:creator>Laziola, Ghora</dc:creator>
<dc:creator>Pahrizal, Pahrizal</dc:creator>
<dc:creator>Apdillah, Dony</dc:creator>
<dc:creator>Ritha, Nola</dc:creator>
<dc:subject xml:lang="en-US"></dc:subject>
<dc:subject xml:lang="en-US">Bintan; Long short-term memory; Machine learning; One-day forecasting; Residual network; Tidal level; Time series forecasting</dc:subject>
<dc:description xml:lang="en-US">The sea brings many benefits for society, especially for a maritime country such as Indonesia. The potential in various sectors is limited only by the willingness of a party to invest in it. One such investment is in learning the knowledge and information that can be gathered from the sea, and even predicting its behavior with enough data. Using a residual LSTM algorithm, we will predict the tidal level in eastern Bintan island, a tropical island on the tip of Malay peninsula. The dataset is acquired from two sensor points in eastern Bintan coast from July 2018 to June 2019 for a span of one year, giving a total of 7,961 data points. The residual LSTM model consists of a residual wrapper with two consecutive LSTM layers and one dense layer. The model is also compared with variations of LSTM and RNN models. The result of the residual LSTM model has an MAE value of 0.1495 cm and an RMSE value of 0.3353 cm, compared to the baseline model’s 1.1148 cm and 1.4107 cm respectively. The model also has an RMSE value improvement of 76.23% compared to the base model.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2024-06-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23847</dc:identifier>
<dc:identifier>10.11591/ijai.v13.i2.pp2003-2010</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 2: June 2024; 2003-2010</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v13.i2</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23847/14009</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
Jumlah menurut tiap indeks pada 13 Nov 2026; setiap indeks menghitung sitasi dengan cara berbeda.
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APA 7
Syakti, A. R., Rhamadhan, S., Laziola, G., Pahrizal, P., Apdillah, D., & Ritha, N. (2024). Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory. IAES International Journal of Artificial Intelligence, 13(2), 2003–2010. https://doi.org/10.11591/ijai.v13.i2.pp2003-2010
Salin
IEEE
A. R. Syakti, S. Rhamadhan, G. Laziola, P. Pahrizal, D. Apdillah, and N. Ritha, “Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory,” IAES International Journal of Artificial Intelligence, vol. 13, no. 2, pp. 2003–2010, 2024, doi: 10.11591/ijai.v13.i2.pp2003-2010.
Salin
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