Prediksi Anomali Kandungan Panas Laut dengan Random Forest Regressor: Pola dan Implikasi Ekologis
Ocean Heat Anomaly; Ecological Impact; Biodiversity; Random Forest Regressor Modeling
Abstract
Indonesian waters are experiencing an anomalous increase in sea heat content, which is impacting aquatic ecosystems. This research aims to predict these anomalies and their implications for marine biodiversity using the Random Forest Regression algorithm. Ocean heat content anomaly data from 2000 to 2023 was processed using Python. The visualization shows an upward trend over the period. The Random Forest Regression model is used with 100 estimators and random_state=42. Performance evaluation was measured by Mean Squared Error (MSE) of 0.2952, Mean Absolute Percentage Error (MAPE) of 2.73%, an R² Score of 0.9848, and an overall accuracy of 97.27%. The most important variable is the year of observation. The prediction results show an anomaly value of 19.66 x 10²² Joules in 2023, which remains relatively stable from 2024 to 2026. This increase has implications for biota metabolism, coral reefs, ocean acidification, species migration, climate variability, and species interactions. Mitigation and adaptation strategies are required to protect marine biodiversity.
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