How to cite this paper
Pontoh, R., Rafi, M., Clorinda, C., Ena, A., Farras, M., Arisanti, R., Toharudin, T & Gumelar, F. (2024). Deep learning approaches to predict sea surface height above geoid in Pekalongan.International Journal of Data and Network Science, 8(2), 743-752.
Refrences
Alghifari, D. R., Edi, M., & Firmansyah, L. (2022). Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia. Jurnal Manajemen Informatika (JAMIKA), 12(2), 89–99. https://doi.org/10.34010/jamika.v12i2.7764
Ambarwari, A., Jafar Adrian, Q., & Herdiyeni, Y. (2020). Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 117–122. https://doi.org/10.29207/resti.v4i1.1517
Bin, Y., Yang, Y., Shen, F., Xie, N., Shen, H. T., & Li, X. (2019). Describing Video With Attention-Based Bidirectional LSTM. IEEE Transactions on Cybernetics, 49(7), 2631–2641. https://doi.org/10.1109/TCYB.2018.2831447
Boehmke, B., & Greenwell, B. (2019). Hands-On Machine Learning with R. Chapman and Hall/CRC. https://doi.org/10.1201/9780367816377
Braakmann-Folgmann, A., Roscher, R., Wenzel, S., Uebbing, B., & Kusche, J. (2017). Sea Level Anomaly Prediction using Recurrent Neural Networks.
Ertugrul, A. M., & Karagoz, P. (2018). Movie Genre Classification from Plot Summaries Using Bidirectional LSTM. 2018 IEEE 12th International Conference on Semantic Computing (ICSC), 248–251. https://doi.org/10.1109/ICSC.2018.00043
Faturrohman, F., & Rosmala, D. (2022, June 21). Analisis Sentimen Sosial Media dengan Metode Bidirectional Gated Re-current Unit. 2022: Prosiding Diseminasi FTI Ganjil 2021/2022.
Fitrinitia, I. S., & Matsuyuki, M. (2022). Role of social protection in coping strategies for floods in poor households: A case study on the impact of Program Keluarga Harapan on labor households in Indonesia. International Journal of Disaster Risk Reduction, 80, 103239. https://doi.org/10.1016/j.ijdrr.2022.103239
GGOS. (2024). Sea Surface Heights. Global Geodetic Observing System.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. The MIT Press.
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM networks. Proceed-ings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2047–2052. https://doi.org/10.1109/IJCNN.2005.1556215
Habibi, S., Pribadi, A., & Sitorus, J. (2021). The concept design for adaptation of climate change through integrated and sustainable flood infrastructure in the coastal area of Pekalongan, Indonesia. Geographica Pannonica, 25(2), 121–135. https://doi.org/10.5937/gp25-30852
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Isnain, A. R., Sihabuddin, A., & Suyanto, Y. (2020). Bidirectional Long Short Term Memory Method and Word2vec Ex-traction Approach for Hate Speech Detection. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(2), 169. https://doi.org/10.22146/ijccs.51743
Jiang, C., Chen, Y., Chen, S., Bo, Y., Li, W., Tian, W., & Guo, J. (2019). A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing. Electronics, 8(2), 181. https://doi.org/10.3390/electronics8020181
Ju, Y., Zhang, M., & Zhu, H. (2019). Study on a New Deep Bidirectional GRU Network for Electrocardiogram Signals Classification. Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Ap-plication Technology (ICCIA 2019). https://doi.org/10.2991/iccia-19.2019.54
Khair, U., Fahmi, H., Hakim, S. Al, & Rahim, R. (2017). Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error. Journal of Physics: Conference Series, 930, 012002. https://doi.org/10.1088/1742-6596/930/1/012002
Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
Liang, S., Wang, D., Wu, J., Wang, R., & Wang, R. (2021). Method of Bidirectional LSTM Modelling for the Atmospheric Temperature. Intelligent Automation & Soft Computing, 29(3), 701–714. https://doi.org/10.32604/iasc.2021.020010
Liliansa, D. (2023, January 19). Sea level rise may threaten Indonesia’s status as an archipelagic country. The Conversa-tion.
Masri, F., Saepudin, D., & Adytia, D. (2020). Forecasting of Sea Level Time Series using Deep Learning RNN, LSTM, and BiLSTM, Case Study in Jakarta Bay, Indonesia. E-Proceeding of Engineering, 8544–8551.
Mehmood, F., Ahmad, S., & Whangbo, T. K. (2023). An Efficient Optimization Technique for Training Deep Neural Net-works. Mathematics, 11(6), 1360. https://doi.org/10.3390/math11061360
NASA. (n.d.). Understanding Sea Level. NASA Sea Level Change Portal. Retrieved July 10, 2023, from https://sealevel.nasa.gov/understanding-sea-level/overview
Nashrrullah, S., Aprijanto, -, Pasaribu, J. M., Hazarika, M. K., & Samarakoon, L. (2014). STUDY ON FLOOD INUNDA-TION IN PEKALONGAN, CENTRAL JAVA. International Journal of Remote Sensing and Earth Sciences (IJReSES), 10(2). https://doi.org/10.30536/j.ijreses.2013.v10.a1845
Panchal, F. S., & Panchal, M. (2014). Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Net-work. International Journal of Computer Science and Mobile Computing, 3(11), 455–464.
Pontoh, R. S., Toharudin, T., Ruchjana, B. N., Gumelar, F., Putri, F. A., Agisya, M. N., & Caraka, R. E. (2022). Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM. Applied Sciences, 12(12), 5771. https://doi.org/10.3390/app12125771
Rayda, N. (2021, March 15). This city in Java could disappear in 15 years, due to land subsidence and coastal flooding. Channel News Asia. https://www.channelnewsasia.com/climatechange/indonesia-pekalongan-land-sinking-coastal-flooding-disappear-1883156
Shahin, A. I., & Almotairi, S. (2021). A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting. Fractal and Fractional, 5(4), 175. https://doi.org/10.3390/fractalfract5040175
Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The Performance of LSTM and BiLSTM in Forecasting Time Se-ries. 2019 IEEE International Conference on Big Data (Big Data), 3285–3292. https://doi.org/10.1109/BigData47090.2019.9005997
Toharudin, T., Pontoh, R. S., Caraka, R. E., Zahroh, S., Lee, Y., & Chen, R. C. (2023). Employing long short-term memory and Facebook prophet model in air temperature forecasting. Communications in Statistics - Simulation and Computa-tion, 52(2), 279–290. https://doi.org/10.1080/03610918.2020.1854302
Vinata, R. T., Kumala, M. T., & Yustisia Serfiyani, C. (2023). Climate change and reconstruction of Indonesia’s geograph-ic basepoints: Reconfiguration of baselines and Indonesian Archipelagic Sea lanes. Marine Policy, 148, 105443. https://doi.org/10.1016/j.marpol.2022.105443
Watson, R. T. (2001). Climate Change 2001: Synthesis Report.
Yu, Z., Sun, Y., Zhang, J., Zhang, Y., & Liu, Z. (2023). Gated recurrent unit neural network (GRU) based on quantile re-gression (QR) predicts reservoir parameters through well logging data. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1087385
Zahroh, S., Hidayat, Y., Pontoh, R. S., Santoso, A., & Talib Bon, A. (2019). Modeling and Forecasting Daily Temperature in Bandung Sukono. Proceedings of the International Conference on Industrial Engineering and Operations Manage-ment Riyadh, Saudi Arabia.
Zhao, Z., Chen, W., Wu, X., Chen, P. C. Y., & Liu, J. (2017). LSTM network: a deep learning approach for short‐term traf-fic forecast. IET Intelligent Transport Systems, 11(2), 68–75. https://doi.org/10.1049/iet-its.2016.0208
Ambarwari, A., Jafar Adrian, Q., & Herdiyeni, Y. (2020). Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 117–122. https://doi.org/10.29207/resti.v4i1.1517
Bin, Y., Yang, Y., Shen, F., Xie, N., Shen, H. T., & Li, X. (2019). Describing Video With Attention-Based Bidirectional LSTM. IEEE Transactions on Cybernetics, 49(7), 2631–2641. https://doi.org/10.1109/TCYB.2018.2831447
Boehmke, B., & Greenwell, B. (2019). Hands-On Machine Learning with R. Chapman and Hall/CRC. https://doi.org/10.1201/9780367816377
Braakmann-Folgmann, A., Roscher, R., Wenzel, S., Uebbing, B., & Kusche, J. (2017). Sea Level Anomaly Prediction using Recurrent Neural Networks.
Ertugrul, A. M., & Karagoz, P. (2018). Movie Genre Classification from Plot Summaries Using Bidirectional LSTM. 2018 IEEE 12th International Conference on Semantic Computing (ICSC), 248–251. https://doi.org/10.1109/ICSC.2018.00043
Faturrohman, F., & Rosmala, D. (2022, June 21). Analisis Sentimen Sosial Media dengan Metode Bidirectional Gated Re-current Unit. 2022: Prosiding Diseminasi FTI Ganjil 2021/2022.
Fitrinitia, I. S., & Matsuyuki, M. (2022). Role of social protection in coping strategies for floods in poor households: A case study on the impact of Program Keluarga Harapan on labor households in Indonesia. International Journal of Disaster Risk Reduction, 80, 103239. https://doi.org/10.1016/j.ijdrr.2022.103239
GGOS. (2024). Sea Surface Heights. Global Geodetic Observing System.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. The MIT Press.
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM networks. Proceed-ings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2047–2052. https://doi.org/10.1109/IJCNN.2005.1556215
Habibi, S., Pribadi, A., & Sitorus, J. (2021). The concept design for adaptation of climate change through integrated and sustainable flood infrastructure in the coastal area of Pekalongan, Indonesia. Geographica Pannonica, 25(2), 121–135. https://doi.org/10.5937/gp25-30852
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Isnain, A. R., Sihabuddin, A., & Suyanto, Y. (2020). Bidirectional Long Short Term Memory Method and Word2vec Ex-traction Approach for Hate Speech Detection. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(2), 169. https://doi.org/10.22146/ijccs.51743
Jiang, C., Chen, Y., Chen, S., Bo, Y., Li, W., Tian, W., & Guo, J. (2019). A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing. Electronics, 8(2), 181. https://doi.org/10.3390/electronics8020181
Ju, Y., Zhang, M., & Zhu, H. (2019). Study on a New Deep Bidirectional GRU Network for Electrocardiogram Signals Classification. Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Ap-plication Technology (ICCIA 2019). https://doi.org/10.2991/iccia-19.2019.54
Khair, U., Fahmi, H., Hakim, S. Al, & Rahim, R. (2017). Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error. Journal of Physics: Conference Series, 930, 012002. https://doi.org/10.1088/1742-6596/930/1/012002
Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
Liang, S., Wang, D., Wu, J., Wang, R., & Wang, R. (2021). Method of Bidirectional LSTM Modelling for the Atmospheric Temperature. Intelligent Automation & Soft Computing, 29(3), 701–714. https://doi.org/10.32604/iasc.2021.020010
Liliansa, D. (2023, January 19). Sea level rise may threaten Indonesia’s status as an archipelagic country. The Conversa-tion.
Masri, F., Saepudin, D., & Adytia, D. (2020). Forecasting of Sea Level Time Series using Deep Learning RNN, LSTM, and BiLSTM, Case Study in Jakarta Bay, Indonesia. E-Proceeding of Engineering, 8544–8551.
Mehmood, F., Ahmad, S., & Whangbo, T. K. (2023). An Efficient Optimization Technique for Training Deep Neural Net-works. Mathematics, 11(6), 1360. https://doi.org/10.3390/math11061360
NASA. (n.d.). Understanding Sea Level. NASA Sea Level Change Portal. Retrieved July 10, 2023, from https://sealevel.nasa.gov/understanding-sea-level/overview
Nashrrullah, S., Aprijanto, -, Pasaribu, J. M., Hazarika, M. K., & Samarakoon, L. (2014). STUDY ON FLOOD INUNDA-TION IN PEKALONGAN, CENTRAL JAVA. International Journal of Remote Sensing and Earth Sciences (IJReSES), 10(2). https://doi.org/10.30536/j.ijreses.2013.v10.a1845
Panchal, F. S., & Panchal, M. (2014). Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Net-work. International Journal of Computer Science and Mobile Computing, 3(11), 455–464.
Pontoh, R. S., Toharudin, T., Ruchjana, B. N., Gumelar, F., Putri, F. A., Agisya, M. N., & Caraka, R. E. (2022). Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM. Applied Sciences, 12(12), 5771. https://doi.org/10.3390/app12125771
Rayda, N. (2021, March 15). This city in Java could disappear in 15 years, due to land subsidence and coastal flooding. Channel News Asia. https://www.channelnewsasia.com/climatechange/indonesia-pekalongan-land-sinking-coastal-flooding-disappear-1883156
Shahin, A. I., & Almotairi, S. (2021). A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting. Fractal and Fractional, 5(4), 175. https://doi.org/10.3390/fractalfract5040175
Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The Performance of LSTM and BiLSTM in Forecasting Time Se-ries. 2019 IEEE International Conference on Big Data (Big Data), 3285–3292. https://doi.org/10.1109/BigData47090.2019.9005997
Toharudin, T., Pontoh, R. S., Caraka, R. E., Zahroh, S., Lee, Y., & Chen, R. C. (2023). Employing long short-term memory and Facebook prophet model in air temperature forecasting. Communications in Statistics - Simulation and Computa-tion, 52(2), 279–290. https://doi.org/10.1080/03610918.2020.1854302
Vinata, R. T., Kumala, M. T., & Yustisia Serfiyani, C. (2023). Climate change and reconstruction of Indonesia’s geograph-ic basepoints: Reconfiguration of baselines and Indonesian Archipelagic Sea lanes. Marine Policy, 148, 105443. https://doi.org/10.1016/j.marpol.2022.105443
Watson, R. T. (2001). Climate Change 2001: Synthesis Report.
Yu, Z., Sun, Y., Zhang, J., Zhang, Y., & Liu, Z. (2023). Gated recurrent unit neural network (GRU) based on quantile re-gression (QR) predicts reservoir parameters through well logging data. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1087385
Zahroh, S., Hidayat, Y., Pontoh, R. S., Santoso, A., & Talib Bon, A. (2019). Modeling and Forecasting Daily Temperature in Bandung Sukono. Proceedings of the International Conference on Industrial Engineering and Operations Manage-ment Riyadh, Saudi Arabia.
Zhao, Z., Chen, W., Wu, X., Chen, P. C. Y., & Liu, J. (2017). LSTM network: a deep learning approach for short‐term traf-fic forecast. IET Intelligent Transport Systems, 11(2), 68–75. https://doi.org/10.1049/iet-its.2016.0208