INTEGRASI LARITA DAN FACE FISH BERBASIS ARTIFICIAL INTELLIGENCE UNTUK PENGUATAN KAPASITAS TEKNOLOGI DAN PENDATAAN DIGITAL PADA PERIKANAN SKALA KECIL
Abstract
Perikanan tangkap skala kecil menghadapi tantangan berupa penggunaan teknologi pencahayaan yang belum efisien, pencatatan hasil tangkapan yang masih manual, serta keterbatasan kapasitas pengelolaan usaha berbasis data. Penelitian ini bertujuan menganalisis implementasi integrasi Lampu Celup Hemat Energi (LARITA) dan Face Fish berbasis Artificial Intelligence (AI) sebagai pendekatan teknologi untuk memperkuat efisiensi operasional, pendataan hasil tangkapan, dan kapasitas manajemen usaha nelayan. Penelitian menggunakan pendekatan pemberdayaan masyarakat melalui penerapan teknologi tepat guna, pelatihan, praktik langsung, pendampingan, dan evaluasi. Peserta kegiatan berjumlah 15 orang yang terlibat dalam aktivitas operasional dan pengelolaan usaha perikanan. LARITA diimplementasikan menggunakan lampu LED berdaya rendah yang ditempatkan di bawah permukaan air, sedangkan Face Fish AI digunakan untuk mendukung identifikasi dan pencatatan hasil tangkapan secara digital. Evaluasi pelatihan menggunakan pretest dan posttest menunjukkan peningkatan nilai rata-rata dari 56,67 menjadi 85,27 atau meningkat 28,60 poin. Sebanyak 13 peserta mampu mengoperasikan LARITA secara mandiri, 12 peserta mencapai kompetensi pemeliharaan dasar, 13 peserta mampu menggunakan Face Fish AI, dan 12 peserta mampu melakukan pencatatan usaha sederhana. Hasil menunjukkan bahwa integrasi teknologi tidak hanya menghasilkan perangkat, tetapi juga membentuk kapasitas pengguna dan sistem pendataan yang lebih terstruktur.
References
Abila, R. O., Ojwang, W., Othina, A., Lwenya, C., Oketch, R., & Okeyo, R. (2013). Using ICT for fish marketing: The EFMIS model in Kenya. Food Chain, 3(1–2), 48–63. https://doi.org/10.3362/2046-1887.2013.005
Allken, V., Handegard, N. O., Rosen, S., Schreyeck, T., Mahiout, T., & Malde, K. (2019). Fish species identification using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science, 76(1), 342–349. https://doi.org/10.1093/icesjms/fsy147
Anabel, N. J., Velvizhi, S., & Suvitha, D. (2018). Fisher Friend Mobile Application: A decision support system for small-scale fishers in India. CSI Transactions on ICT, 6, 257–267. https://doi.org/10.1007/s40012-018-0212-1
Arimoto, T., Choi, H., & Arakawa, Y. (2016). Behavioral responses of fish to light and its application to fishing techniques. Fisheries Science, 82(5), 685–695. https://doi.org/10.1007/s12562-016-1011-3
Bui, C. B., Nguyen, K. Q., Winger, P. D., & Grant, S. M. (2021). Use of light-emitting diode (LED) lamps in combination with metal halide (MH) lamps reduce fuel consumption in the Vietnamese purse seine fishery. Aquaculture and Fisheries, 6(4), 432–440. https://doi.org/10.1016/j.aaf.2020.07.011
Catalán, I. A., Álvarez-Ellacuría, A., Lisani, J.-L., Sánchez, J., Vizoso, G., Heinrichs-Maquilón, A. E., Hinz, H., Alós, J., Signarioli, M., Aguzzi, J., Francescangeli, M., & Palmer, M. (2023). Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training. Frontiers in Marine Science, 10, 1151758. https://doi.org/10.3389/fmars.2023.1151758
FAO. (2022). The State of World Fisheries and Aquaculture 2022: Towards Blue Transformation. FAO. https://doi.org/10.4060/cc0461en
Geissdoerfer, M., Savaget, P., Bocken, N. M. P., & Hultink, E. J. (2017). The Circular Economy – A new sustainability paradigm? Journal of Cleaner Production, 143, 757–768. https://doi.org/10.1016/j.jclepro.2016.12.048
Hilborn, R. (2020). Effective fisheries management instrumental in improving fish stock status. Proceedings of the National Academy of Sciences, 117(4), 2218–2224. https://doi.org/10.1073/pnas.1909726116
Jalal, A., Salman, A., Mian, A., Shortis, M., & Shafait, F. (2020). Fish detection and species classification in underwater environments using deep learning with temporal information. Ecological Informatics, 57, 101088. https://doi.org/10.1016/j.ecoinf.2020.101088
Jareño, J., Bárcena-González, G., Castro-Gutiérrez, J., Cabrera-Castro, R., & Galindo, P. L. (2024). Automatic labeling of fish species using deep learning across different classification strategies. Frontiers in Computer Science, 6, 1326452. https://doi.org/10.3389/fcomp.2024.1326452
Knausgård, K. M., Wiklund, A., Sørdalen, T. K., Halvorsen, K. T., Kleiven, P. J. N., Jiao, L., & Goodwin, M. (2022). Temperate fish detection and classification: A deep learning based approach. Applied Intelligence, 52, 6988–7001. https://doi.org/10.1007/s10489-021-02771-4
Li, X., Shang, M., Qin, H., & Chen, L. (2015). Fast accurate fish detection and recognition of underwater images with Fast R-CNN. OCEANS 2015–MTS/IEEE Washington, 1–5. https://doi.org/10.23919/OCEANS.2015.7404464
Marrable, D., Barker, K., Tippaya, S., Wyatt, M., Bainbridge, S., Stowar, M., & Larke, J. (2022). Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning. Frontiers in Marine Science, 9, 944582. https://doi.org/10.3389/fmars.2022.944582
Nguyen, K. Q., & Lee, K. W. (2017). Energy efficiency and catch performance of LED fishing lamps in small-scale fisheries. Fisheries and Aquatic Sciences, 20(1). https://doi.org/10.1186/s41240-017-0065-8
Nguyen, K. Q., Winger, P. D., Morris, C., & Grant, S. M. (2022). Light-emitting diode (LED) lights reduce the fuel consumption and maintain the catch rate of stick-held falling net fisheries. Regional Studies in Marine Science, 52, 102542. https://doi.org/10.1016/j.rsma.2022.102542
Park, J. H., & Lee, J. W. (2018). Fishing lamp technology for energy saving and catch efficiency. Journal of the Korean Society of Fisheries Technology, 54(3), 189–197. https://doi.org/10.3796/KSFT.2018.54.3.189
Ruan, Z., Wang, Z., & He, Y. (2024). DeformableFishNet: A high-precision lightweight target detector for underwater fish identification. Frontiers in Marine Science, 11, 1424619. https://doi.org/10.3389/fmars.2024.1424619
Salman, A., Jalal, A., Shafait, F., Mian, A., Shortis, M., Seager, J., & Harvey, E. S. (2016). Fish species classification in unconstrained underwater environments based on deep learning. Limnology and Oceanography: Methods, 14(9), 570–585. https://doi.org/10.1002/lom3.10113
Siri, D., Vellaturi, G., Shaik Ibrahim, S. H., Molugu, S., Desanamukula, V. S., Kocherla, R., & Vatambeti, R. (2024). Enhanced deep learning models for automatic fish species identification in underwater imagery. Heliyon, 10(15), e35217. https://doi.org/10.1016/j.heliyon.2024.e35217
Sofijanto, M. A., Arfiati, D., Lelono, T. D., & Muntaha, A. (2019). Efficiency comparison of LED and MH lamps in purse seine fisheries. Turkish Journal of Fisheries and Aquatic Sciences, 19(2), 41–49. https://doi.org/10.4194/1303-2712-v19_2_05
Susanto, A., Irnawati, R., Mustahal, & Syabana, M. A. (2017). Fishing efficiency of LED lamps for fixed lift net fisheries in Banten Bay, Indonesia. Turkish Journal of Fisheries and Aquatic Sciences, 17(2), 283–291. https://doi.org/10.4194/1303-2712-v17_2_07
Thenu, I. M., Puspito, G., & Martasuganda, S. (2016). The use of light emitting diode on sunked lamps of lift net. Marine Fisheries, 4(2), 141–151. https://doi.org/10.29244/jmf.4.2.141-151
Villon, S., Mouillot, D., Chaumont, M., Darling, E. S., Subsol, G., Claverie, T., & Villéger, S. (2018). A deep learning method for accurate and fast identification of coral reef fishes in underwater images. Ecological Informatics, 48, 238–244. https://doi.org/10.1016/j.ecoinf.2018.09.007
Yu, M., Liu, C., Zhang, L., & Tang, Y. (2022). Application of light-emitting diodes (LEDs) fishing lights to improve catch rates of small-scale trammel net fishery in the Yellow Sea, China. Frontiers in Marine Science, 9, 1036979. https://doi.org/10.3389/fmars.2022.1036979
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