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All Journal Indonesian Journal of Electronics and Instrumentation Systems Journal of Maritime Empowerment Journal of Fisheries & Marine
Lestari, Nurhaliza Amalia
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Electrical & Electronics Engineering Environmental Science Social Sciences

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Classification of KJA Net Conditions Using ROV and Computer Vision Lestari, Nurhaliza Amalia; Jaya, Indra; Rahmat, Ayi; Hestirianoto, Totok
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 14, No 1 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.91891

Abstract

The development and integration of Remotely operated vehicle (ROV) with computer vision has been carried out and shows excellent performance. All ROV features functions run smoothly and without problems and are able to monitor the condition of nets in floating net cages (KJA) and produce underwater videos. Data collected from ROV are processed, utilizing the YOLOv8 model and showed very positive results in classifying the condition of KJA nets. The model achieves an accuracy level of 1 or 100% differentiate between clean and dirty net. Based on these results, it can be concluded that the YOLOv8 model has excellent performance in recognizing mesh objects with a high level of accuracy. These results provide confidence that this model can be trusted in monitoring the condition of KJA nets.
PEMANFAATAN TEKNOLOGI TUNNEL GARAM DI DESA PANIMBANGJAYA, KABUPATEN PANDEGLANG Aziizah, Nunung Noer; Lestari, Nurhaliza Amalia; Khalifa, Muta Ali; Hasanah, Afifah Nurazizatul; Munandar, Erik; Wantona, Nico; Supadminingsih, Fahresa Nugraheni; Jasmine, Agitha Saverthi; Dewantara, Esza Cahya; Akif, Muhammad Mahdi; Fitria, Syarlla Putri Ara
Jurnal Pemberdayaan Maritim Vol 8 No 1 (2025): Journal of Maritime Empowerment
Publisher : Lembaga Penelitian, Pengabdian Masyarakat, dan Penjaminan Mutu, Universitas Maritim Raja Ali Haji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31629/jme.v8i1.7658

Abstract

Produksi garam di Indonesia masih sangat bergantung pada kondisi cuaca, sehingga kualitas dan jumlah yang dihasilkan sering tidak stabil. Desa Panimbangjaya, Kabupaten Pandeglang, menghadapi permasalahan serupa dalam produksi garam rakyat. Kegiatan pengabdian ini memperkenalkan tiga komponen inovasi teknologi tunnel garam yaitu kolam penuaan berlapis HDPE, filterisasi air laut, dan dinding tunnel portabel kepada siswa MAN 3 Pandeglang sebagai agen transfer pengetahuan. Metode pelaksanaan meliputi sosialisasi, pelatihan praktik, diskusi interaktif, dan evaluasi melalui kuesioner. Data dianalisis secara deskriptif kuantitatif dan kualitatif. Hasil survei menunjukkan skor persepsi rata-rata 4,65 (kategori sangat baik), dengan penilaian tertinggi pada aspek keberlanjutan teknologi tunnel garam (5,0). Responden menekankan kebutuhan penambahan jumlah tunnel, replikasi ke desa pesisir lain, pelatihan lanjutan, serta dukungan pemasaran. Temuan ini menunjukkan bahwa keterlibatan siswa tidak hanya memperkuat transfer pengetahuan lintas generasi, tetapi juga mendukung keberlanjutan inovasi produksi garam rakyat di wilayah pesisir.
Assessment of Fish Floating Net Cage Conditions Using ROV and Deep Learning Algorithms Lestari, Nurhaliza Amalia; Jaya, Indra
Jurnal Ilmiah Perikanan dan Kelautan 2026: JIPK VOLUME 18 ISSUE 2 YEAR 2026 (JUNE 2026, ISSUE IN PROGRESS)
Publisher : Faculty of Fisheries and Marine Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jipk.v18i2.85796

Abstract

Graphical Abstract Highlight Research 1. This study demonstrates the successful use of a self-developed Remotely Operated Vehicle (ROV) to acquire underwater imagery for monitoring floating net cage conditions without the need for manual diving. 2. The integration of ROV-based image acquisition with deep learning classification using the YOLOv8 model achieved high accuracy in identifying different levels of net fouling under real aquaculture field conditions. 3. The results show that classification performance decreases as the number of fouling classes increases, influenced by visual similarity between classes, environmental variability, and limited image distribution. 4. The proposed approach provides an effective, non-invasive, and practical monitoring solution that supports timely maintenance decisions and contributes to more sustainable management of floating net cage aquaculture systems.   Abstract Monitoring the condition of floating net cages (FNC) is essential for maintaining water circulation, dissolved oxygen availability, and overall fish health in aquaculture systems. However, FNC-based aquaculture commonly faces the problem of biofouling accumulation, including barnacles, algae, sediment, dirt, and solid waste, which gradually obstruct water flow and reduce cage performance. This study aimed to develop an automated method for classifying floating net cage fouling conditions by integrating a self-developed remotely operated vehicle (ROV) with deep learning–based underwater image classification. Underwater monitoring produced 7,156 extracted image frames, which were processed through image selection and white balance color correction. A total of 741 images were used to train a YOLOv8 model under three classification schemes, namely 2-class, 3-class, and 6-class classifications. The results demonstrated high classification performance across all schemes, with accuracy values of 100% for the 2-class model, 99% for the 3-class model, and 98% for the 6-class model. These findings indicate that integrating ROV-based image acquisition with deep learning classification provides an effective approach for assessing floating net cage conditions, enabling timely maintenance, improving monitoring efficiency, and supporting better environmental management in aquaculture systems. Future studies are encouraged to expand the dataset size and environmental variability to further enhance model robustness.
Co-Authors Akif, Muhammad Mahdi Dewantara, Esza Cahya Fahresa Nugraheni Supadminingsih, Fahresa Nugraheni Fitria, Syarlla Putri Ara Hasanah, Afifah Nurazizatul Hestirianoto, Totok Indra Jaya Jasmine, Agitha Saverthi Khalifa, Muta Ali Munandar, Erik Nunung Noer Aziizah, Nunung Noer Rahmat, Ayi RR. Ella Evrita Hestiandari Wantona, Nico