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All Journal Jurnal Sumberdaya Akuatik Indopasifik Albacore : Jurnal Penelitian Perikanan Laut Asian Journal of Aquatic Sciences Semah : Jurnal Pengelolaan Sumberdaya Peraira Jurnal Perikanan dan Kelautan
Muhammad Natsir Kholis
Fakultas Perikanan Universitas Muara Bungo, Jl. Pangeran Diponegoro, Cadika, Rimbo Tengah, Kabupaten Bungo, Jambi 37211-Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Earth & Planetary Sciences Energy Environmental Science Materials Science & Nanotechnology Physics

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EFEKTIVITAS BUBU LIPAT PAYUNG UNTUK MENANGKAP IKAN SELUANG (Rasbora argyotaenia) DI SUNGAI MENTENANG KECAMATAN JANGKAT KABUPATEN MERANGIN PROVINSI JAMBI Dicky Kurniadi; Syafrialdi Syafrialdi; Muhammad Natsir Kholis
SEMAH Jurnal Pengelolaan Sumberdaya Perairan Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36355/semahjpsp.v6i2.944

Abstract

Bubu is a passive fishing gear designed to catch various types of fish, crustaceans, mollusks and others, with various shapes and made of various materials. One type is a folding umbrella trap which is now widely operated in the waters of Jangkat, Jambi. The main catch target is seluang fish. The purpose of the research was to determine the effect of the number of entrances (funnels) on the average catch and to determine the effectiveness of umbrella traps. The research method is experimental fishing. The results showed that there was a difference in the average catches of funnel 6 and funnel 8 traps based on the results of the calculation of T hit (2.85) T tab (2.18). Umbrella traps of funnel 6 and funnel 8 are not effective in catching seluang fish (Rasbora argyotaenia) because E 1. However, folding umbrella traps can be said to be effective in catching seluang fish when viewed from the number of catches per species, not based on target and non-target fish.
Fishing Gear Selectivity of Wire Trap to Limbat Fish (Clarias nieuhofii) in Swamp Water Rimbo Ulu, Tebo Regency, Jambi Province Muhammad Natsir Kholis; Yudha Maulana Syuhada
Jurnal Perikanan dan Kelautan Vol. 26 No. 2 (2021): June
Publisher : Faculty of Fisheries and Marine, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jpk.26.2.126-130

Abstract

This study aims to analyze the level of selectivity of wire trap fishing gear against limbat fish (Clarias nieuhofii) in the swamp waters of Tebo Regency, Jambi Province. Data collection was carried out by catching trials using 3 units of wire traps with 30 replications, in June-August 2020. The research data were analyzed descriptively using the logistical selectivity model of the maximum likelihood method equation in the solver program from microsoft excel. The results showed that the wire traps were not selective for size but were selective for the limbat fish species (C.nieuhofii). The selectivity curve based on the logistical function shows that the probability of being caught by fish are at 22-48 mm body height, while the size of the fish that can escape has a maximum height of 34 mm or a length of 182 mm
WATER QUALITY MANAGEMENT TRANSFORMATION THROUGH DEEP LEARNING: FROM LABORATORY TO LARGE-SCALE IMPLEMENTATION (OCEAN) Nur, M Irsyad; Aprisanti, Rizka; Kurniawan, Ronal; Yulindra, Ade; Azuga, Nabila Afifah; Kholis, M Natsir; Limbong, Irwan
Asian Journal of Aquatic Sciences Vol. 8 No. 1 (2025): April
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ajoas.8.1.102-109

Abstract

The exponential growth of environmental challenges, particularly those affecting water resources, necessitates innovative technological interventions beyond conventional approaches. This review explores the transformative potential of deep learning technologies in water quality management across different scales from controlled laboratory environments to complex oceanic systems. By analyzing recent developments, we identify how neural networks, especially convolutional and recurrent architectures, have revolutionized water quality parameter prediction, anomaly detection, and ecosystem monitoring. Integrating multi-modal data streams with advanced algorithms has enabled unprecedented predictive accuracy and real-time assessment capabilities, transforming reactive monitoring systems into proactive management frameworks. Despite significant progress, challenges remain in data standardization, model interpretability, and the practical deployment of these technologies in resource-constrained settings. This review critically assesses current research trajectories and identifies promising avenues for future development, emphasizing the importance of interdisciplinary collaboration in translating laboratory innovations to large-scale implementation for safeguarding our most precious resource
Co-Authors Dahlia Dahlia Dicky Kurniadi Edwarsyah Edwarsyah Fraternesi Harizon Harizon Hehanussa, Kedswin Gerson Irwan Limbong Jaliadi . La Ode Wahidin M Irsyad Nur, M Irsyad Made Mahendra Jaya Mohd. Yusuf Amrullah Mustaruddin Nabila Afifah Azuga Rini Hertati Rizka Aprisanti Roma Yuli Hutapea Ronal Kurniawan Ronny I. Wahju Sri Maryeni Syafrialdi Syafrialdi Tri Nanda Citra Bangun Yudha Maulana Syuhada Yulindra, Ade Yusuf Rohadi