Classification Of Sea Wave Heights On The North Coast Of Central Java Using Random Forest - Supriyanto, Aji; Diartonor, Dwi Agus; Hartono, Budi; Jananto, Arief; Afandi, Afandi (Jurnal Teknik Informatika (Jutif), 2025)
Global climate change has triggered an increase in the occurrence of significant wave heights (SWH) and sea level rise (SLR) in coastal areas, including the northern coast of Central Java, Indonesia (Pantura). These phenomena directly impact maritime activities, coastal erosion, and tidal flooding. This study aims to classify and predict significant wave height (SWH) and sea level rise (SLR) trends using a machine learning approach based on the Random Forest (RF) algorithm. Daily meteorological and oceanographic observation data from 2019 to 2024, provided by BMKG, serve as the main dataset. The dataset includes wind speed, ocean current velocity, air pressure, and wave direction. SWH is categorized into three classes: Calm, Low, and Moderate. The classification model achieved excellent performance with an accuracy of 98.54%, a macro F1-score of 0.942, and maintained strong accuracy even for the minority class (Moderate) despite data imbalance. The RF Regressor for SWH prediction yielded an R² of 0.864, MAE of 0.067, and RMSE of 0.109 m. Visualizations such as scatter plots, boxplots, and heatmaps supported the conclusion that ocean current speed and wave period are key factors influencing SWH. The study concludes that Random Forest is effective for classifying and predicting sea conditions in tropical regions like Pantura, and it is feasible for implementation in data-driven early warning systems to mitigate coastal risks. This contributes to marine safety and coastal risk mitigation planning.