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All Journal Jurnal Ilmu Pertanian Indonesia Buletin PSP Jurnal Teknologi Perikanan dan Kelautan Marine Fisheries: Jurnal Teknologi dan Manajemen Perikanan Laut Maspari Journal Maspari Journal Jurnal Perikanan dan Kelautan Journal of Aquatropica Asia Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat Albacore : Jurnal Penelitian Perikanan Laut Bambu Laut: Jurnal Pengabdian Masyarakat
Prihatin Ika Wahyuningrum
Departemen Pemanfaatan Sumberdaya Perikanan, Fakultas Perikanan Dan Ilmu Kelautan Institut Pertanian Bogor, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Library & Information Science Social Sciences

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DESIGN AND DEVELOPMENT OF A DEEP LEARNING-BASED AUTOMATIC FISHERIES LOGBOOK FILLING TOOL Iqbal, Muhammad; Tanhir, Muhammad Fadhilah; Apriliansyah, Bachtiar Adi; Pratama, Xavercius Cezar; Nadja, Kuntum Khaira; Lotaldy, Alnodio; Wahyuningrum, Prihatin Ika
Marine Fisheries : Journal of Marine Fisheries Technology and Management Vol. 15 No. 2 (2024): Marine Fisheries: Journal of Marine Fisheries Technology and Management
Publisher : Bogor Agricultural University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jmf.v15i2.47574

Abstract

Filling out a fishery logbook is critical in capturing fisheries to maintain fishery sustainability. However, manual logbook filling often leads to errors in data recording, which can impact the accuracy and quality of fishery data. This study propose the design of an automatic fisheries logbook filling tool based on Deep Learning, called FLOADS (Fisheries Logbook and Detection System). This system consists of instruments placed at the ship's hatch, equipped with a camera capable of detecting fish species, measuring the weight of individual fish passing through, and recording and sending data, along with the ship's position and speed, to a server on land. The development stages of the FLOADS include problem identification, tool design, prototype development, and field testing. The FLOADS prototype consisted of three parts: a fish collection container, a fish sliding area, and an Imaging Box with dimensions of 120 cm × 65 cm × 82 cm. Validation of the YOLOv5 model showed an accuracy rate of 99.07% and an F1-Score of 99.1% for recognizing the Tuna, Cakalang, and Tongkol (TCT) fish species. Field testing was conducted in Palabuhanratu using a 10 GT fishing vessel. The field testing results show that FLOADS performs well on board to detect objects with an accuracy rate of 89.1% and track vessel activities with reasonable accuracy. Keywords: Deep Learning, DeepSORT, Fisheries Logbook, Measurable Fishing, TCT.
Co-Authors Agung Nugraha Am Azbas Taurusman Andi Gustomi Apriliansyah, Bachtiar Adi Aulia Putra Bangun, Tri Nanda Citra Bayu Wiratama Budy Wiryawan Dianti, Sari Rama Domu Simbolon Dwi Putra Yuwandana Eko Sri Wiyono Elvanri Anggi Widianti Elvanri Anggi Widianti Eva Utami Fis Purwangka Gilar Budi Pratama Gondo Puspito Hapsari, Rianti Dyah Hapsari, Riyanti Dyah Hardiansyah, Ridwan Hendro Wahyudi Huwaida, Hanifah Ignatius Viktor Deornay Iin Solihin Iin Solihin Indra Jaya Irfan Yulianto Julia Eka Astarini Khoerunnisa, Nurani Lotaldy, Alnodio Mokhamad Dahri Iskandar Muhammad Dahri Iskanda Muhammad Fedi Alfiadi Sondita Muhammad Iqbal Muhammad Iqbal Mulyono S. Baskoro Mustaruddin Nadja, Kuntum Khaira Nur Muhammad Syadli Nurani Khoerunnisa Nurani Khoerunnisa Pratama, Xavercius Cezar Rahmi, Tiara Anggia Regi Darmawan Retno Muninggar Rianti Dyah Hapsari Rika Rizkawati Risah Palevi Risti Endriani Arhatin Roisul Maarif Ronny I. Wahju Roza Yusfiandayani Silvia Silvia T. Ersti Yulika Sari Tanhir, Muhammad Fadhilah Tiara Anggia Rahmi Tri Nanda Citra Bangun Tri W Nurani Tri Wiji Nurani Usman Usman Vita R. Kurniawati Vita Rumanti Kurniawati Widianti, Elvanri Anggi Yoga Nugraha Yopi Novita