Machine Learning Based Water Level Forecasting for Tidal Flood Mitigation in Indonesian Coastal Regions: Peramalan Ketinggian Air Berbasis Pembelajaran Mesin untuk Mitigasi Banjir Pasang Surut di Wilayah Pesisir Indonesia - Sugiarto Sugiarto; Riduan Riduan; Linus Yoseph Wawan Rukmono (Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang, 2026)
Changes in coastal water levels significantly impact maritime logistics and tidal flood risks, which are categorised as non-military threats to national security and socio-geographical stability. This study aims to develop and compare water level prediction models using Random Forest, Long Short-Term Memory (LSTM), and their ensemble as a technology-based disaster mitigation strategy in Indonesian coastal areas. The dataset was obtained from the Tanjung Priok Maritime AWS in August 2024 and comprises meteorological features (rainfall, air temperature, air pressure, wind speed) and oceanographic data (sea surface temperature), with water level as the target variable. Data preprocessing involved time-based linear interpolation and feature standardisation. Model evaluation was conducted using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and residual analysis. The results indicate that Random Forest provides stable performance (RMSE 0.2957, MAE 0.2130), while LSTM is more responsive to daily temporal fluctuations despite slightly higher errors (RMSE 0.3156, MAE 0.2339). A simple ensemble of both models achieved the most optimal and robust performance (RMSE 0.2954, MAE 0.2132) with a well-distributed residual. This study concludes that implementing the ensemble model enhances the reliability of tidal flood early warning systems, serving as a crucial pillar of non-military defence strategies to safeguard national logistical security in Indonesia's coastal zones.