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All Journal Jurnal Perikanan dan Kelautan Journal of Environmental Engineering and Sustainable Technology JFMR (Journal of Fisheries and Marine Research) Omni-Akuatika International Journal of Community Service Learning PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Perikanan dan Kelautan
Fuad, Mochamad Arif Zainul
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Pemetaan Kerentanan Tsunami di Kawasan Wisata Pantai: Studi Kasus di Pulau Merah, Banyuwangi-Indonesia: Tsunami Vulnerability Mapping in Coastal Tourism Areas: A Case Study of Merah Island, Banyuwangi - Indonesia Fuad, Mochamad Arif Zainul; Kurniasari, Diah; Dewi, Citra Satrya Utama
JFMR (Journal of Fisheries and Marine Research) Vol. 9 No. 2 (2025): JFMR on July
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.2025.009.02.2

Abstract

Pantai selatan Jawa merupakan kawasan yang rentan terhadap tsunami karena berbatasan langsung dengan zona subduksi antara Lempeng Eurasia dan Lempeng Australia. Kejadian tsunami telah melanda beberapa wilayah di Indonesia dan menyebabkan kerugian yang cukup besar, baik korban jiwa maupun kerusakan infrastruktur. Oleh karena itu, upaya mitigasi risiko bencana perlu diterapkan di wilayah-wilayah rawan bencana di Indonesia, salah satunya melalui pemetaan kerentanan tsunami dengan memanfaatkan Sistem Informasi Geografis (SIG). Penelitian ini bertujuan untuk menganalisis kerentanan wilayah pesisir pariwisata di Desa Sumberagung, Kecamatan Pesanggaran, Kabupaten Banyuwangi terhadap tsunami berdasarkan beberapa parameter, yaitu kemiringan lahan, elevasi daratan, penggunaan lahan, serta jarak dari garis pantai dan sungai. Hasil penelitian menunjukkan bahwa kemiringan lahan di wilayah penelitian berkisar antara 3-5%, yang termasuk dalam kategori rentan terhadap genangan tsunami, dengan elevasi kurang dari 10 meter. Penggunaan lahan di wilayah ini didominasi oleh kawasan hutan yang dikategorikan tidak rentan. Namun, terdapat sungai yang bermuara di laut, yang memungkinkan gelombang tsunami menjangkau lebih jauh ke daratan melalui aliran sungai. Berdasarkan hasil analisis menggunakan metode Weighted Overlay Analysis, wilayah pesisir Desa Sumberagung diklasifikasikan ke dalam kategori kurang rentan(48.2%), rentan (28.6%), dan sangat rentan (11.0%). Meskipun wilayah dengan kategori sangat rentan memiliki luas yang lebih kecil, area ini merupakan kawasan yang padat penduduk dan menjadi penunjang destinasi wisata utama di Banyuwangi, yaitu Pulau Merah.Hasil penelitian ini mengindikasikan pentingnya mitigasi dan kesiapsiagaan terhadap potensi kejadian Tsunami di wilayah resiko tinggi seperti di lokasi kajian.   The southern coast of Java is highly susceptible to tsunamis due to its direct adjacency to the subduction zone between the Eurasian and the Australian Plates. Indonesia has faced multiple tsunami events, resulting in severe casualties and extensive damage to infrastructure. Therefore, it is imperative that effective disaster risk mitigation strategies are implemented in vulnerable regions in Indonesia, particularly through comprehensive tsunami vulnerability mapping using Geographic Information Systems (GIS). This study decisively evaluates the vulnerability of coastal tourism areas in Sumberagung Village, Pesanggaran District, Banyuwangi Regency, to tsunamis. The analysis is based on critical parameters, including land slope, elevation, land use, and distance from the coastline and rivers. The results show that the land slope in the study area ranges from 3-5%, categorizing it as vulnerable, with elevations under 10 meters. The predominant land use consists of forest areas, classified as not vulnerable, however, the existence of rivers flowing into the sea significantly increases the risk of tsunami-invading rivers. Employing the Weighted Overlay Analysis method, we classified the coastal areas of Sumberagung Village into three categories: less vulnerable (48.2%), vulnerable (28.6%), and very vulnerable (11.0%). Notably, despite the relatively small size of the very vulnerable category, it is densely populated and includes Pulau Merah, one of Banyuwangi’s premier tourist destinations. This analysis underscores the urgent need for informed planning and effective disaster preparedness in these high-risk areas.
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 Aida Sartimbul Bambang Semedi Defri Yona Dewi, Citra Satrya Utama Feni Iranawati, Feni Haq, Nuril Annisa Hardiansyah, Fikri Hidayati, Nurin Hidayatullah, Fathurrosyid Kartikaningsih, Hartati Kurniasari, Diah Ledhyane Ika Harlyan Lestariadi, Riski Agung Masdian, Nuril Haq Bilqishima Nabila, Risda Ayu Prasetio, Rachmat Ramadhan Pudji Purwanti Putra, Valeryan Ramana Rahman, Alham Danendra Rahmanissa, Aulia Intan Rahmawati, Andira Rijal, Seftiawan Syamsu Setyawan, Fahreza Okta Setyoningrum, Desy Ussania, Qoirunnisa Wardani, Aisy Nur Isna Zannuar, Rizky Wisudawan