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All Journal Jurnal Kelautan : Indonesian Journal of Marine Science and Technology TRITON : Jurnal Manajemen Sumberdaya Perairan Balobe: Jurnal Pengabdian Masyarakat
ADHITYA NUGROHO
Pattimura University
Economics, Econometrics & Finance Education Environmental Science Social Sciences Other

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Pemetaan Habitat Bentik Berbasis Objek Menggunakan Drone Di Perairan Pulau Gili Labak, Sumenep Adhitya Nugroho; Bisma Nababan; James Parlindungan Panjaitan; Syamsul Bahri Agus
Jurnal Kelautan Vol 17, No 1: April (2024)
Publisher : Department of Marine Sciences, Trunojoyo University of Madura, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/jk.v17i1.24518

Abstract

ABSTRAKPemetaan habitat bentik menggunakan drone memiliki kendala terkait kodisi cuaca dan lingkungan, seperti kecepatan angin dan sun glint yang dapat mengganggu pengambilan gambar dan klasifikasi gambar. Penelitian ini bertujuan untuk mengetahui sudut sensor drone yang optimal, waktu penerbangan drone terbaik di lokasi penelitian, serta mengetahui tingkat akurasi algoritma support vector machine menggunakan metode OBIA. Penelitian ini dilaksanakan di perairan Pulau Gili Labak pada bulan Oktober 2022 menggunakan drone DJI Phantom 4. Penelitian ini menerapkan dua sudut sensor 45° dan 90° serta waktu pengambilan pukul 08:00; 09:30; 13:15; 14:45. Klasifikasi citra drone menggunakan metode OBIA menggunkan metode contextual editing pada level 1 (perairan dangkal). Level 2 menggunakan klasifikasi terbimbing menggunakan algoritma klasifikasi machine learning yaitu support vector machine (SVM) dengan input themathic layer dari data lapangan. Klasifikasi habitat bentik dilakukan pada 6 kelas dengan penerapan skala segmentasi 25, 50, 70, 100. Berdasarkan hasil  pengambilan gambar waktu terbaik menerbangkan drone pada pukul 13:15 menggunakan sudut sensor 90º dilokasi penelitian, diperoleh nilai overall accuracy sebesar 84.06% serta nilai kappa 0.78656 pada skala segmentasi 50 dengan algoritma support vector machine.Kata kunci: pemetaan, habitat bentik, OBIA, drone, Pulau Gili LabakABSTRACTBenthic habitat mapping using drones has constraints related to weather and environmental conditions, such as wind speed and sun glint that can interfere with image capture and image classification. This study aims to determine the optimal drone sensor angle, the best drone flight time at the research location, and determine the accuracy of the support vector machine algorithm using the OBIA method. This research was conducted in the waters of Gili Labak Island in October 2022 using a DJI Phantom 4 drone. This research applied two sensor angles of 45° and 90° and the capture time at 08:00; 09:30; 13:15; 14:45. Classification of drone imagery using the OBIA method utilizes contextual editing at level 1 (shallow water). Level 2 uses guided classification using a machine learning classification algorithm, namely support vector machine (SVM) with themathic layer input from field data. Benthic habitat classification was performed on 6 classes with the application of segmentation scales of 25, 50, 70, 100. Based on the results of taking pictures of the best time to fly the drone at 13:15 using a 90º sensor angle at the research location, an overall accuracy value of 84.06% was obtained and a kappa value of 0.78656 on a segmentation scale of 50 with the support vector machine algorithm. Keywords: Mapping, Benthic Habitats, OBIA, Gili Labak Island 
SOSIALISASI PENINGKATAN KAPASITAS KELEMBAGAAN DAN RASIONALISASI TARIF WISATA PANTAI BERBASIS NILAI EKONOMI DI NEGERI HUKURILA, KOTA AMBON Nugroho, Adhitya; Ruban, Angela; Talakua, Eygner G.; Papilaya, Renoldy L.; Sangaji, Janer
Balobe: Jurnal Pengabdian Masyarakat Vol 4 No 2 (2025): Balobe: Jurnal Pengabdian Masyarakat
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/balobe.4.2.82-88

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilakukan untuk memberikan rekomendasi solusi bagi pengelolaan wisata pantai berkelanjutan di Negeri Hukurila, Kota Ambon, yang merupakan objek wisata unggulan dengan potensi sumber daya pesisir, perikanan, dan kelautan yang besar. Meskipun memiliki nilai ekonomi kawasan yang tinggi (mencapai Rp 120.760.533/bulan), pengelolaan saat ini menghadapi tantangan serius, termasuk rendahnya kunjungan, kualitas produk wisata yang kurang, tarif wisata yang sangat rendah, ancaman deplesi lebar pantai, dan status kelembagaan POKMASWAS yang lemah. Berfokus pada dua masalah manajemen utama—kurangnya jumlah kunjungan dan rendahnya tarif wisata—solusi diwujudkan melalui sosialisasi dan Focus Group Discussion (FGD). Hasil PkM menunjukkan bahwa mitra menyetujui dan berkomitmen untuk melaksanakan program yang diusulkan, yaitu pengelolaan wisata pantai yang memanfaatkan potensi kelautan dan perikanan, didukung penuh dengan penetapan peraturan desa.
Comparative Performance Evaluation Of Machine Learning Algorithms For Sentinel-2 Benthic Habitat Classification Using Google Earth Engine Adhitya Nugroho; Muhammad Abdul Ghofur Al Hakim
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
PERAN GENDER DAN KERENTANAN PENGHIDUPAN RUMAH TANGGA NELAYAN KEPITING BAKAU TERHADAP PERUBAHAN IKLIM DI DESA BULA AIR Nanlohy, Hellen; Walla, Sitti Aisah; Nugroho, Adhitya
TRITON: Jurnal Manajemen Sumberdaya Perairan Vol 22 No 1 (2026): TRITON: Jurnal Manajemen Sumberdaya Perairan
Publisher : Departement of Aquatic Resources Management, Fisheries and Marine Science Faculty, Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/TRITONvol22issue1page1-10

Abstract

Climate change affects gender roles in the mangrove crab fishing businesses. Women play a role in crab fishing and are more vulnerable to the impacts of climate change due to limited access to resources. This study aims to analyze gender roles in the mangrove crab fishing businesses and to examine the impact of climate change on the household income of fishermen in Bula Air Village, East Seram Regency. The research was conducted from January to June 2025. This study used a Likert scale (1–5) approach with 40 respondents. Correlation analysis results indicate a significant negative relationship between the impacts of climate change and income (r = -0.642; p < 0.05). Simple linear regression analysis indicates that the impacts of climate change significantly influence a decrease in income (β = -0.587; p = 0.000) with a coefficient of determination (R²) of 0.412. This means that 41.2% of the variation in income is influenced by the impacts of climate change. These findings underscore the importance of integrating a gender perspective into fisheries adaptation policies to enhance the resilience of coastal fishermen’s livelihoods. ABSTRAK Perubahan iklim mempengaruhi peran gender dalam usaha penangkapan kepiting bakau. Perempuanmempunyai peran dalam usaha penangkapan kepiting dan akan lebih rentan terhadap dampak perubahan iklim karena akses terbatas terhadap sumberdaya. Penelitian ini bertujuan menganalisis peran gender dalam usaha penangkapan kepiting bakau serta menguji pengaruh dampak perubahan iklim terhadap pendapatan rumah tangga nelayan di Desa Bula Air, Kabupaten Seram Bagian Timur. Penelitian dilakukan pada bulan Januari-Juni 2025. Penelitian ini menggunakan pendekatan skala Likert (1–5) terhadap 40 responden. Hasil analisis korelasi menunjukkan hubungan negatif signifikan antara dampak perubahan iklim dan pendapatan (r = -0,642; p<0,05). Analisis regresi linear sederhana menunjukkan bahwa dampak perubahan iklim berpengaruh signifikan terhadap penurunan pendapatan (β = -0,587; p = 0,000) dengan koefisien determinasi (R²) sebesar 0,412. Artinya, 41,2% variasi pendapatan dipengaruhi oleh dampak perubahan iklim. Temuan ini menegaskan pentingnya integrasi perspektif gender dalam kebijakan adaptasi perikanan untuk meningkatkan ketahanan penghidupan nelayan pesisir. Kata kunci: Gender, kepiting bakau, perubahan iklim, pesisir, kerentanan nelayan
Co-Authors Angela Ruban, Angela Bisma Nababan Hellen Nanlohy James Parlindungan Panjaitan Janer Sangaji, Janer Muhammad Abdul Ghofur Al Hakim Papilaya, Renoldy L. Syamsul Bahri Agus, Syamsul Bahri Talakua, Eygner G. Walla, Sitti Aisah