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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis Journal of Coastal and Ocean Sciences
Mas’ud M, Ahmad Azwar
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Environmental Science Immunology & microbiology Library & Information Science Physics

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Analysis of Wave Characteristic in Fuel Terminal Serui Mas’ud M, Ahmad Azwar; Suntoyo, Suntoyo; Pratikto, Widi A
Journal of Coastal and Ocean Sciences Vol. 5 No. 1 (2024): January
Publisher : Department of Marine Science, Faculty of Fisheries and Marine Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jocos.5.1.17-26

Abstract

The Serui Fuel Terminal is a vital state-owned facility distributing fuel oil throughout Yapen Island, Papua Province. However, this facility has a problem: waves or overtopping, which can even cause damage to existing facilities. This research aims to determine the hydrodynamic process and wave characteristics through theoretical analysis and numerical modeling using Mike21 with Hydrodynamic (HD) and Spectral Wave (SW) Modules. Bathymetry, current and tidal data collected in the field and wave data collected from ECMWF, calibration is carried out by comparing modeling output (currents & tides) with the results of observations and wave propagation and transformation study theoretically and compared with the results of wave modeling, so the accuracy of the modeling results can be reviewed. The validation results of tidal modeling with a MAPE (Mean Absolute Percentage Error) value of 0.3%, current modeling with a MAPE value of 30%, and waves from each orthogonal with an average MAPE of 15%. Generally, wave height on the shoreline is 0.3-0.4 m (calm waves), and the cause of the overtopping that occurs is due to the geometry and type of existing coastal buildings with smooth sloping sides and impermeable, which makes the wave height double with a run-up height 0.6-0.8 m
Machine Learning-Based Mapping of Mangrove Forest Changes from Sentinel-2 in Balikpapan Bay, East Kalimantan Al Hakim, Muhammad Abdul Ghofur; Sinurat, Maya Eria Br; Zulkifle, Nurul Ain Najwa; Nurmawati; Mas’ud M, Ahmad Azwar
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 17 No. 3 (2025): 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.17.3.67707

Abstract

Balikpapan Bay contains extensive mangrove forests which play an important role as habitat for a range of species and in providing a range of ecosystem services. In recent years, the mangrove forests around Balikpapan Bay are increasingly being lost and degraded due to development pressures. Thus, change detection in mangrove ecosystem has become highly relevant, as it can provide essential information to support the conservation practices and coastal management. This study aims to map mangrove forest change in Balikpapan Bay, East Kalimantan over a five-year period from Sentinel-2 using machine learning. Five machine learning algorithms (Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), K-Nearest Neighbors (KNN), and Minimum Distance), implemented on the Google Earth Engine platform, were evaluated to determine the most suitable method. The evaluation results indicate that RF, SVM, and CART yielded mangrove mapping accuracies of 80% or higher. Notably, the CART algorithm surpassed the other tested models, demonstrating the highest overall accuracy of 84% and a Kappa coefficient of 0.78. Mapping using the selected CART model shows that, between 2020 and 2025, mangrove areas in Balikpapan Bay decreased by 21% (2,906.17 ha). Approximately 97% (2,834.49 ha) of this loss is concentrated in the North Penajam Paser, which has a high rate of land conversion to built-up areas.
Co-Authors Al Hakim, Muhammad Abdul Ghofur Maya Eria Br Sinurat, Maya Eria Br Nurmawati Pratikto, Widi A Zulkifle, Nurul Ain Najwa