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Putri Suhendi, Brigitta Aurelia
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Integrasi Citra Satelit Radar dan Data AIS untuk Monitoring Tumpahan Minyak dengan Pendekatan Machine Learning Putri Suhendi, Brigitta Aurelia; Marsisno, Waris
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2318

Abstract

Oil spill cases are routine occurrences in the Riau Archipelago Province, particularly in the northern part of Bintan Island, as one of the consequences of the island's strategic location near Singapore, a global trade and financial hub. The limitations of resources and data pose obstacles for the government in addressing this issue. Therefore, in this study, the author offers a solution by detecting oil spill areas using Sentinel-1 radar data and wind speed using the adaptive thresholding method, the best classification model from 7 machine learning algorithms and 2 deep learning algorithms. Based on the accuracy, precision, recall, and F1-score values of these 9 algorithms, the best algorithm obtained is XGBoost (Extreme Gradient Boosting). Followed by mapping and estimating the area of the oil spill at 177.54 ha, as well as identifying ships passing through the oil spill area using AIS (Automatic Identification System) data and wind direction, with the result being 2 ships passing through the area.
Co-Authors Marsisno, Waris