Implementation of LSTM Method on Tidal Prediction in Semarang Region

Semarang is the capital of the Central Java province, located in the north and directly adjacent to the Java Sea. Having an almost flat land condition with a slope of about 0-2%, Semarang City has the opportunity to experience tidal flooding. The occurrence of tides does not have a fixed period. So, it is necessary to predict the height of the tide and the ebb of the seawater. Thus, this research aims to predict tides in the Semarang area using the LSTM method. The data used is tidal data in Semarang waters from 2020 to 2024. The advantage of the LSTM method is its ability to effectively remember time series data or data with long-term dependence. LSTM can store past information using special cells contained in its structure. This research on tidal prediction using the LSTM method with 70% training data trial batch size 32 and epoch 200 obtained the smallest error value, namely the MAE value of 0.0388 and MAPE of 0.0313 which is the best LSTM result.

Authors

Institutions

Publication Details

Journal
Journal of Applied Informatics and Computing
Published
2025-03-17
DOI
https://doi.org/10.30871/jaic.v9i2.8932
Primary Topic
Geotechnical and construction materials studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Implementation of LSTM Method on Tidal Prediction in Semarang Region

Moh. Hafiyusholeh, Dian Candra Rini Novitasari, Yuniar Farida, Fajar Setiawan et al.
Journal of Applied Informatics and Computing
Geotechnical and construction materials studies
article

Implementation of LSTM Method on Tidal Prediction in Semarang Region

Moh. Hafiyusholeh, Dian Candra Rini Novitasari, Yuniar Farida, Fajar Setiawan, Panreshma Rizkha Ambadar
article en

Abstract

Semarang is the capital of the Central Java province, located in the north and directly adjacent to the Java Sea. Having an almost flat land condition with a slope of about 0-2%, Semarang City has the opportunity to experience tidal flooding. The occurrence of tides does not have a fixed period. So, it is necessary to predict the height of the tide and the ebb of the seawater. Thus, this research aims to predict tides in the Semarang area using the LSTM method. The data used is tidal data in Semarang waters from 2020 to 2024. The advantage of the LSTM method is its ability to effectively remember time series data or data with long-term dependence. LSTM can store past information using special cells contained in its structure. This research on tidal prediction using the LSTM method with 70% training data trial batch size 32 and epoch 200 obtained the smallest error value, namely the MAE value of 0.0388 and MAPE of 0.0313 which is the best LSTM result.

Journal of Applied Informatics and ComputingVol. 9(2)
Meteorological, Climatological, And Geophysical Agency (ID), UIN Sunan Ampel Surabaya (ID)
Openalex Percentile: Top 91%
Geotechnical and construction materials studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Implementation of LSTM Method on Tidal Prediction in Semarang Region — Moh. Hafiyusholeh, Dian Candra Rini Novitasari, et al. · Journal of Applied Informatics and Computing (2025) | TGRS Research Map | TGRS