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All Journal Jurnal Indonesia Sosial Sains
Supriyade Supriyade
Universitas Esa Unggul, Indonesia
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Neuroscience Physics Social Sciences

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Analysis of Time Series Water Level Data Prediction Using Deep Learning Method at the Water Gate of DKI Jakarta Water Resources Office Supriyade Supriyade; Gerry Firmansyah; Habibullah Akbar; Budi Tjahjono
Jurnal Indonesia Sosial Sains Vol. 4 No. 09 (2023): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v4i09.883

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
Co-Authors Budi Tjahjono Gerry Firmansyah Habibullah Akbar