Prediksi Tinggi Gelombang Laut di Perairan Semarang – Demak dengan Menggunakan Random Forest dan XGBoost - Erutjahjo, Ganis; Supriyanto, Aji (Politeknik Harapan Bersama, 2025)
English Climate change and regional oceanographic activities have contributed to increasing significant wave height (SWH) and sea level rise (SLR) along the northern coast of Central Java (Semarang-Demak). This study aims to analyze and predict SWH and SLR using two artificial intelligence methods: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The dataset includes meteorological and oceanographic parameters from 2019 to 2024. Model performance was evaluated using accuracy metrics such as RMSE, MAE, MAPE, and the coefficient of determination (R²). The results show that XGBoost outperforms RF in predicting both target variables. XGBoost achieved R² values of 0.9989 for SWH and 0.9921 for SLR, with MAPE scores of 1.6% and 1.1%, respectively. The most influential factor for SWH prediction was the historical significant wave height (hs), while the average daily sea level elevation had the highest impact on SLR prediction. Comparison plots between actual and predicted values indicate that both models effectively captured seasonal trends, particularly in identifying wave peaks in early months and sea level increases during mid-year.The 2025 forecast suggests rising SWH patterns from January to March and peak SLR values around June. These findings are expected to support coastal adaptation policies in response to climate change and to inform the design of more resilient marine infrastructure in the future.