Article Per Year (5 Year)
p-Index From 2021 - 2026
2.195
P-Index
This Author published in this journals
All Journal Journal of Environmental Engineering and Sustainable Technology JFMR (Journal of Fisheries and Marine Research) Jurnal Kelautan Nasional PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Chart Datum Jurnal Pengabdian Masyarakat (ABDIRA) Abdi Geomedisains
Setyawan, Fahreza Okta
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Analisis Tingkat Akurasi Data Batimetri Menggunakan Singlebeam Echosounder System (Sbes) dan Citra Satelit Spot-7 di Perairan Gili Ketapang, Kabupaten Probolinggo Laily, Hilda Hikmatul; Setyawan, Fahreza Okta; Fuad, M. Arif Zainul; Nandika, Muhammad Rizki
Jurnal Kelautan Nasional Vol 19, No 1 (2024): APRIL
Publisher : Pusat Riset Kelautan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/jkn.v19i1.13381

Abstract

Pulau Gili Ketapang memiliki jumlah potensi sumberdaya perikanan mencapai 40%, sehingga Gili Ketapang berpotensi menjadi daerah perikanan dengan sumberdaya yang cukup tinggi dan pelayaran yang cukup padat. Hal ini berarti informasi mengenai batimetri di perairan tersebut bermanfaat untuk mengetahui kondisi perairan, baik secara langsung maupun tidak langsung. Penelitian ini dilakukan untuk mengetahui tingkat akurasi data batimetri dari Singlebeam Echosounder System (SBES) dan citra satelit SPOT-7, sehingga dapat digunakan sebagai referensi kedalaman perairan. Teknik yang digunakan dalam penelitian ini merupakan Satellite Derived Bathymetry (SDB) metode klasifikasi Random Forest dengan menggunakan software Satellite Derived Bathymetry (SDB) GUI. Kedalaman perairan di Pulau Gili Ketapang berdasarkan hasil pemeruman berkisar antara 0-30m. Profil kedalaman perairan pada peta batimetri 3D menunjukkan bahwa morfologi dasar laut tidak rata. Profil kedalaman dari garis pantai hingga jarak 200 m memiliki nilai kedalaman 0-20 m, sedangkan profil kedalaman dari jarak 200m hingga perairan terbuka cukup bervariasi. Klasifikasi kelas lereng di perairan Pulau Gili Ketapang didominasi oleh kategori datar sebesar 77,28%. Hasil regresi (R²) pada model SDB antara data citra dengan data insitu yaitu 0,59 atau 59%. Nilai RMSE yang tertinggi terdapat pada kedalaman 0 – 5m yaitu 0,87, sedangkan yang terendah pada kedalaman 15,1 - 20m yaitu 1,31.
Analisis Akurasi Data Batimetri Single Beam Echosounder System (SBES) dan Satellite Derived Bathymetry (SDB) (Studi Kasus Perairan Kabupaten Cilacap, Jawa Tengah): Accuracy Analysis of Single Beam Echosounder System (SBES) and Satellite Derived Bathymetry (SDB) Data (Cased Study the Waters of Cilacap Regency, Central Java) Setyawan, Fahreza Okta; Rahman, Alham Danendra; Hidayati, Nurin; Fuad, Mochamad Arif Zainul; Semedi, Bambang; Setyoningrum, Desy
JFMR-Journal of Fisheries and Marine Research Vol. 10 No. 1 (2026): JFMR on March
Publisher : Faculty of Fisheries and Marine Science, Brawijaya University, Malang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jfmr.2026.010.01.4

Abstract

Hidrografi dan batimetri menyediakan informasi kedalaman dan bentuk dasar perairan yang sangat penting bagi navigasi, perencanaan pelabuhan, rekayasa pantai, dan tata ruang laut, terutama pada perairan dinamis dan keruh seperti Kabupaten Cilacap yang berbatasan langsung dengan Samudera Hindia. Penelitian ini bertujuan menganalisis akurasi data batimetri Satellite Derived Bathymetry (SDB) berbasis citra PlanetScope beresolusi tinggi dan membandingkannya dengan hasil pemeruman Single Beam Echosounder System (SBES). Data SBES dikumpulkan menggunakan Garmin 585 C, sedangkan kedalaman SDB diekstraksi dengan algoritma machine learning Generalized Additive Model (GAM). Data dibagi 75% untuk training dan 25% untuk testing, dan kinerja model dinilai menggunakan koefisien determinasi (R²) dan Root Mean Square Error (RMSE). Pengolahan data didukung perangkat lunak SIG dan penginderaan jauh seperti ArcMap, SNAP, ENVI, Global Mapper, RStudio, dan Surfer. Hasil menunjukkan SBES memiliki akurasi tinggi dan tetap menjadi rujukan utama pemetaan dasar perairan Cilacap. Sebaliknya, model SDB–GAM menghasilkan R² sebesar 0,35 dan RMSE 2,80 sehingga belum mampu merepresentasikan variasi kedalaman pada perairan. Nilai ini rendah karena perairan Cilacap yang keruh serta keberadaan sunglint pada citra menyebabkan distorsi reflektansi sehingga kedalaman hasil SDB sulit merepresentasikan variasi kedalaman aktual. Oleh karena itu, SDB – GAM belum direkomendasikan untuk pemetaan operasional di perairan keruh seperti Cilacap tanpa koreksi glint dan peningkatan strategi pemodelan, serta lebih tepat digunakan sebagai pemetaan awal pada kondisi perairan yang lebih jernih dan kedalaman terbatas.   Hydrography and bathymetry provide information on water depth and seabed morphology that is crucial for navigation, port planning, coastal engineering, and marine spatial planning, especially in dynamic and turbid waters such as Cilacap Regency, which borders directly with the Indian Ocean. This study aims to analyze the accuracy of bathymetric data Satellite Derived Bathymetry (SDB) derived from high-resolution PlanetScope imagery and compare it with obtained from Single Beam Echosounder System (SBES) sounding. SBES data were collected using a Garmin 585 C, while SDB depths were extracted using the Generalized Additive Model (GAM) machine learning algorithm. Data were split into 75% for training and 25% for testing, and model performance was evaluated using the coefficient of determination (R²) and Root Mean Square Error (RMSE). Data processing was supported by GIS and remote sensing software such as ArcMap, SNAP, ENVI, Global Mapper, RStudio, and Surfer. The results show that SBES provides high accuracy and remains the primary reference for seabed mapping in Cilacap waters. Conversely, the SDB–GAM model produces an R² of 0.35 and an RMSE of 2.80, indicating that it has not yet adequately represented depth variability in waters. This value is low because the murky waters of Cilacap and the presence of sunglint in the image cause reflectance distortion, making it difficult for the SDB results to represent actual depth variatons. Therefore, SDB-GAM is not yet recommended for operational mapping in murky waters such as Cilacap without glint correction and improved modeling strategies, and is more appropriately used for initial mapping in clearer waters and limited depths.
Comparative Analysis of SBES Bathymetry and Machine Learning-Based Satellite-Derived Bathymetry from Sentinel-2 in the Coastal Waters of Dubibir, Situbondo Fuad, Mochamad Arif Zainul; Zannuar, Rizky Wisudawan; Rijal, Seftiawan Syamsu; Setyawan, Fahreza Okta
JFMR-Journal of Fisheries and Marine Research Vol. 10 No. 1 (2026): JFMR on March
Publisher : Faculty of Fisheries and Marine Science, Brawijaya University, Malang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jfmr.2026.010.01.13

Abstract

Accurate Bathymetryc information in shallow coastal waters is critical for marine navigation, coastal zone management, habitat assessment, and environmental monitoring. Conventional Bathymetryc surveys are limited by high operational costs, restricted spatial coverage, and time-consuming fieldwork. To overcome these challenges, the present study assesses the potential of Satellite-Derived Bathymetry (SDB) using Sentinel-2 imagery as an alternative, comparing it with in situ Bathymetryc measurements obtained via Single Beam Echosounder (SBES) in the coastal waters of Dubibir, Situbondo, Indonesia. Bathymetryc data were collected with an SBES GPSMAP 585c, and Sentinel-2 multispectral imagery was processed to estimate water depth using a Random Forest (RF) machine learning model. Results indicate that SBES measurements reached a maximum depth of 53.34 m, while Sentinel-2-derived bathymetry captured depths up to 29.61 m. Model evaluation yielded a coefficient of determination (R²) of 0.83, a mean absolute error (MAE) of 1.90 m, and a root mean square error (RMSE) of 3.56 m, demonstrating strong predictive performance in shallow-water environments. However, the findings also show reduced SDB capability in deeper, optically complex waters, particularly where turbidity limits light penetration and weakens the satellite signal. Overall, Sentinel-2 imagery combined with the RF algorithm offers a practical, cost-effective, and spatially efficient solution for shallow-water Bathymetryc mapping, while SBES remains essential for validation and for representing deeper seabed conditions. This study advances the application of machine learning-based SDB for coastal mapping and provides a relevant approach for generating Bathymetryc data in data-limited coastal regions.
Co-Authors Ade Yamindago Aida Sartimbul Aliviyanti, Dian Andik Isdianto Ash Shufi, Abdillah Hanan Astuti, Retno Tri Bambang Semedi Daduk Setyohadi Darmawan Ockto Sutjipto Defri Yona Farikhah, Reghita Dwi Firdaus, Naufal Fuad, M. Arif Zainul Fuad, Mochamad Arif Zainul Fuad, Muhammad Arif Zainul Guntur Haq, Nuril Annisa Hidayati, Nurin Hidayatullah, Fathurrosyid Krisnanto, Wahyu Fajar Laily, Hilda Hikmatul Maftuch Maftuch Nandika, Muhammad Rizki Rahman, Alham Danendra Rahman, M. Arif Rijal, Seftiawan Syamsu Risqi, Mohammad Aditya Saphira, Shada Sari, Wahida Kartika Seftiawan Samsu Rijal, Seftiawan Samsu Setyoningrum, Desy Syarifah Hikmah Julinda Sari Ussania, Qoirunnisa Widodo S Pranowo Zannuar, Rizky Wisudawan