Pemetaan Otomatis Air Permukaan dengan Pendekatan Random Forest dan Citra Sentinel-2

Understanding the presence and distribution of the Earth's surface water is crucial for various environmental planning segments such as water resources management, natural disaster mitigation, environmental conservation, and spatial planning. In addition, the availability of Sentinel-2 satellite imagery data that offers high spatial resolution with wide coverage allows for more accurate and detailed surface water mapping compared to traditional methods. This research presents the utilization of the Random Forest algorithm as a surface water mapping model tested on Sentinel-2 image data and can be applied nationally with a spatial resolution of 10 meters. In this case, the Random Forest model was developed using spectral indices such as Modified Normalized Difference Water Index (MNDWI) and Normalized Vegetation Index (NDVI) as well as true color (RGB) spectral bands. The accuracy assessment results show that the Random Forest model built can perform surface water mapping very accurately, which is confirmed by the evaluation metrics in the form of overall accuracy, average producer's accuracy, and average user's accuracy which have a value of 98.12%, 98.13%, and 98.07%, respectively. Based on these results, it can be seen that the model has significant potential to assist the environmental planning process further through surface water mapping.