Klasifikasi Citra Sentinel melalui Google Earth Engine dengan menggunakan algoritma Machine Learning XGBoost - Gregorius Anung Hanindito; Adi Wibowo; Budi Warsito (InComTech : Jurnal Telekomunikasi dan Komputer, 2026)
Remote sensing technology and Geographic Information Systems (GIS) have rapidly evolved to provide extensive data and information on land cover. This study aims to monitor land cover in Tanjung Keluang Nature Tourism Park (TWA) and its surroundings using Sentinel satellite imagery on the Google Earth Engine (GEE) platform, employing the XGBoost machine learning algorithm. The methods involved acquiring Sentinel satellite imagery, pre-processing for geometric correction, developing training and testing datasets, as well as performing classification and accuracy evaluation. The results indicate that the XGBoost algorithm can classify land cover into several categories with an accuracy of up to 98%. The classified land cover includes water bodies (23,346 Ha), open land (9,680.54 Ha), sand mining areas (931.15 Ha), and vegetation (16,596.84 Ha). This study contributes positively to the management of conservation areas, particularly in supporting decision-making for TWA Tanjung Keluang in the future.