Analysis of Time Series Water Level Data Prediction Using Deep Learning Method at the Water Gate of DKI Jakarta Water Resources Office

Authors

  • Supriyade Universitas Esa Unggul, Indonesia
  • Gerry Firmansyah Universitas Esa Unggul, Indonesia
  • Habibullah Akbar Universitas Esa Unggul, Indonesia
  • Budi Tjahjono Universitas Esa Unggul, Indonesia

DOI:

https://doi.org/10.59141/jiss.v4i09.883

Keywords:

Prediction of water level Deep Learning, Long Short Term Memory (LSTM), Recurrent Neural Network

Abstract

Indonesia has 2 seasons, namely the dry season and the rainy season. During the rainy season, many points in the DKI Jakarta area experience flooding or inundation. The reason why Jakarta often experiences flooding is caused by several factors, including local rain floods, shipment floods and tidal floods. The DKI Jakarta Water Resources Agency currently does not have a system that can predict future water levels by referring to past and present water level data. Through this background, the author tries to conduct research in one of the floodgates in the northern area of DKI Jakarta in predicting water levels using deep learning methods , namely Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM). The purpose of this research is to analyze the best deep learning models and predict water level time series data. From the results of the analysis carried out, the best deep learning model is Long Short Term Memory (LSTM) using several tests such as n-input, split data with a composition of 90.33% train data and 9.67% test data , as well as testing of different parameters including epoch, batch size, learning rate, dropout , so the results obtained are the lowest error values with RMSE (17.65), MAPE (0.29), MAE (3.37) and the time needed in the process (runtime) is 39 minutes

References

Fredrik, Joan Arnold, Fredrik, Joan Arnold, Sudinda, Teddy W., Sejati, Wahyu, Fredrik, Joan Arnold, & Pendahuluan, A. (2021). ANALISIS TINGGI MUKA AIR PADA KAWASAN SUNGAI CILIWUNG MT . HARYONO – PINTU AIR MANGGARAI DENGAN PROGRAM HEC-RAS 4 . 1 . 0 ANALYSIS OF WATER LEVEL IN THE CILIWUNG RIVER AREA MT . HARYONO – MANGGARAI WATER GATE WITH HEC-RAS 4 . 1 . 0 PROGRAM. 100–106.

Hastomo, Widi, Karno, Adhitio Satyo Bayangkari, Kalbuana, Nawang, Nisfiani, Ervina, & ETP, Lussiana. (2021). Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 7(2), 133. https://doi.org/10.26418/jp.v7i2.47411

Jakarta, Posko Banjir DKI. (2020). Data Pintu Air DKI Jakarta.

Journal, Jambura, & Mathematics, O. F. (2023). Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network. 5(1), 230–242.

Lattifia, Tita, Wira Buana, Putu, & Rusjayanthi, Ni Kadek Dwi. (2022). Model Prediksi Cuaca Menggunakan Metode LSTM. JITTER Jurnal Ilmiah Teknologi Dan Komputer, 3(1), 994–1000.

Lstm, Algoritma, Kota, Di, Sugiartawan, Putu, & Santoso, Servus Gusprio. (n.d.). Multivariate Forecasting Curah Hujan Menggunakan. 580–585.

Naik, Dr Poornima G., & Girish, Dr R. Nai. (2021). Conseptualizing Python in Google Colab. Sashwat Publication.

Pane, Syafrial Fachric, & Yogi Aditya Saputra. (2020). Big Data classification behavior menggunakan python. Kreatif Industri Nusantara.

Putri Sekti Ari, Dessanti, & Hanum, Latifah. (2021). Pengaruh Kualitas Pelayanan Website Djp Terhadap Kepuasan Pengguna Dengan Modifikasi E Govqual. Profit, 15(01), 104–111. https://doi.org/10.21776/ub.profit.2021.015.01.11

Rizki, Muhammad, Basuki, Setio, & Azhar, Yufis. (2020). Implementasi Deep Learning Menggunakan Arsitektur Long Short Term Memory(LSTM) Untuk Prediksi Curah Hujan Kota Malang. Jurnal Repositor, 2(3), 331–338. https://doi.org/10.22219/repositor.v2i3.470

Sanjaya, David, & Budi, Setia. (2020). Prediksi Pencapaian Target Kerja Menggunakan Metode Deep Learning dan Data Envelopment Analysis. Jurnal Teknik Informatika Dan Sistem Informasi, 6(2), 288–300. https://doi.org/10.28932/jutisi.v6i2.2678

Downloads

Published

2023-08-25

How to Cite

Supriyade, S., Firmansyah, G. ., Akbar, H. ., & Tjahjono, B. . (2023). Analysis of Time Series Water Level Data Prediction Using Deep Learning Method at the Water Gate of DKI Jakarta Water Resources Office. Jurnal Indonesia Sosial Sains, 4(09), 753–762. https://doi.org/10.59141/jiss.v4i09.883

Issue

Vol. 4 No. 09 (2023): Jurnal Indonesia Sosial Sains

Section

Articles

License

Copyright (c) 2023 Supriyade, Gerry Firmansyah, Habibullah Akbar, Budi Tjahjono

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International. that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.