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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis Jurnal Kelautan : Indonesian Journal of Marine Science and Technology
Al Hakim, Muhammad Abdul Ghofur
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Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Library & Information Science

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DETEKSI TURBIDITY FRONT MENGGUNAKAN CITRA SATELIT SENTINEL-2 HUBUNGANNYA DENGAN OSEANOGRAFI DI ESTUARI BENGAWAN SOLO Susilo, Setyo Budi; Gaol, Jonson Lumban; Al Hakim, Muhammad Abdul Ghofur
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 14 No. 3 (2022): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v14i3.40172

Abstract

Estuari merupakan daerah percampuran antara massa air tawar dan air laut yang menyebabkan zat-zat di dasar perairan naik ke permukaan sehingga konsentrasi unsur hara menjadi tinggi. Penelitian mengenai pertemuan massa air estuari masih perlu dilakukan terutama terkait turbidity front estuary karena untuk mengetahui kemampuan citra Setinel-2 dalam mendeteksi turbidity front. Selama ini penelitian ini terbatas dari data in situ, oleh karena itu teknologi penginderaan jauh coba diterapkan untuk mendeteksi turbidity front estuary. Penelitian ini bertujuan untuk mengembangkan algoritma TSS lokal dan mendeteksi turbidity front berdasarkan citra satelit Sentinel-2. Metode penelitian ini menggunakan citra Sentinel-2 untuk mengetahui batas turbidity front berdasarkan TSS yang dibandingan dengan data in situ salinitas dan TSS sebagai validasi data. Hasil penelitian ini diketahui algoritma empiris yang diperoleh dari band ratio (merah/(biru+hijau+merah)) pada Sentinel-2 memiliki hasil yang terbaik dengan koefisien determinasi (R2) = 0,7409. Hasil citra satelit menunjukkan bahwa turbidity front estuary terjadi pada jarak 1,4 – 3 km, sedangkan pada data in situ terjadi pada jarak 2 – 4 km di muara Bengawan Solo. Terdapat perbedaan nilai TSS sebesar 1,9182 mg/L antara data in situ dengan citra satelit di daerah turbidity front estuary. Kondisi musim, curah hujan dan pasang surut memengaruhi konsentrasi dan jarak turbidity front dari muara sungai.
Comparative Performance Evaluation Of Machine Learning Algorithms For Sentinel-2 Benthic Habitat Classification Using Google Earth Engine Nugroho, Adhitya; Al Hakim, Muhammad Abdul Ghofur
Jurnal Kelautan Vol 18, No 3: Desember (2025)
Publisher : Department of Marine Sciences, Trunojoyo University of Madura, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/jk.v18i3.32389

Abstract

This study evaluates the optimal classification methodology and analyzes temporal changes over five years across four benthic habitat classes (Seagrass, Coral Reef, Rubble, and Sand) in the shallow waters of Ohoidertawun, Southeast Maluku, using Sentinel-2 imagery and the Google Earth Engine (GEE) platform. A comparative assessment of Machine Learning (ML) algorithms revealed that Random Forest (RF) demonstrated the best classification performance compared to Support Vector Machine (SVM), Classification and Regression Tree (CART), K-Nearest Neighbors (KNN), and Minimum Distance (MD) in benthic habitat mapping, achieving an Overall Accuracy of 0.856 and a Kappa Coefficient of 0.870. The classification results and accuracy assessment using the best-performing ML model from the 2025 Sentinel-2 imagery were used to analyze temporal changes relative to the 2020 Sentinel-2 data. Temporal analysis indicated a significant ecosystem shift, marked by a 52.41% increase in seagrass cover and a 31.46% decrease in coral reef area. These findings can serve as a recommendation for conservation site selection and urge stakeholders to help mitigate coral reef loss by utilizing the results of this research. The resulting benthic habitat map can serve as a reference for effective coastal resource management and blue carbon initiatives. Based on these findings, the Random Forest ML algorithm can be considered an optimal methodology for tropical benthic habitat mapping in the study area.Keywords: Benthic Habitat, Sentinel-2, Machine Learning, Google Earth Engine
Co-Authors ADHITYA NUGROHO Jonson Lumban Gaol Setyo Budi Susilo