Classification of Mangrove Ecosystem Health Using Sentinel-2 Images with Genetic Algorithm Optimization in Machine Learning Algorithms

Mangrove ecosystems play an important role in maintaining coastal ecological balance, including as carbon sinks and natural protection from abrasion, but mangrove areas in Mempawah Regency have experienced significant degradation due to anthropogenic pressures. Therefore, this study aims to classify the health condition of mangroves using multi-temporal Sentinel-2 imagery with a hybrid machine learning (ML) approach and Genetic Algorithm (GA) optimization. We implemented GA optimization comparatively on four main ML models—Multilayer Perceptron (MLP), Decision Tree (DT), XGBoost, and Naïve Bayes (NB)—to adjust hyperparameters to improve accuracy and reduce overfitting. The results prove that GA optimization effectively improves classification performance, with the MLP-GA model providing the highest accuracy with an increase of up to 3.8% compared to the non-optimized baseline model, achieving a best performance value of ROC AUC 0.9730 and reducing computation time by up to 60%. These findings indicate that the GA-MLP framework is highly reliable and efficient, providing a precise tool for strategic decision-making in the management of healthy mangrove ecosystems.