Identifikasi Intensitas Makan Ikan Budidaya Akuaponik berdasarkan Kualitas Air dan Pergerakan Ikan

MULKI REZKA BUDI PRATAMA, REZA FIKRI ALFATAH, JAYA KUNCARA ROSA SUSILA

Sari


Abstrak

Pemberian pakan ikan dapat ditentukan melalui pengamatan perilaku, kualitas air, dan ukuran ikan. Salah satu metode otomatisasi yang digunakan adalah ANFIS (Adaptive Neuro Fuzzy Inference System), dengan masukan berupa kualitas air (kekeruhan dan NH3) serta aktivitas gerak ikan menggunakan inframerah (IR). Sistem ini mendukung keputusan pemberian pakan secara optimal. Validasi alat dilakukan menggunakan model regresi linier dan evaluasi kinerja berdasarkan Mean Relative Error (MRE). Hasil menunjukkan akurasi perangkat mencapai 95,77%, lebih tinggi 6,55% dibandingkan perangkat tanpa kualitas air (89,22%). Model ini terbukti andal dan dapat diterapkan pada sistem akuaponik berbasis industri untuk meningkatkan efisiensi pemberian pakan ikan.

 

Kata kunci: Akuaponik; biologi otomatis; rekayasa sistem; robotika; instrumentasi

Abstract

Fish feeding can be determined by observing behavior, water quality, and fish size. One automation method used is ANFIS (Adaptive Neuro Fuzzy Inference System), which uses inputs such as water quality (turbidity and NH3) and fish movement activity detected by infrared (IR). This system supports optimal feeding decision-making. The tool validation was conducted using a linear regression model, and its performance was evaluated based on the Mean Relative Error (MRE). The results showed that the device achieved an accuracy of 95.77%, 6.55% higher than devices without water quality input (89.22%). This model has proven reliable and can be applied to industry-based aquaponic systems to enhance the efficiency of fish feeding.

Keywords: Aquaponics; automated biology; systems engineering; robotics; instrumentation


Teks Lengkap:

PDF

Referensi


Bartley, D. M. (n.d.). WORLD AQUACULTURE 2020 A BRIEF OVERVIEW. https://doi.org/10.4060/cb7669en

Bucking, C. (2016). A broader look at ammonia production, excretion, and transport in fish: a review of impacts of feeding and the environment. Journal of Comparative Physiology B 2016 187:1, 187(1), 1–18. https://doi.org/10.1007/S00360-016-1026-9

Buerger, A. N., Parente, C. E., Harris, J. P., Watts, E. G., Wormington, A. M., & Bisesi, J. H. (2022). Impacts of diethylhexyl phthalate and overfeeding on physical fitness and lipid mobilization in Danio rerio (zebrafish). Chemosphere, 295, 133703. https://doi.org/10.1016/J.CHEMOSPHERE.2022.133703

Coldebella, A., Gentelini, A. L., Piana, P. A., Coldebella, P. F., Boscolo, W. R., & Feiden, A. (2017). Effluents from Fish Farming Ponds: A View from the Perspective of Its Main Components. Sustainability 2018, Vol. 10, Page 3, 10(1), 3. https://doi.org/10.3390/SU10010003

de Alba, G., Conti, F., Sánchez, J., Godoy, L. M., Sánchez-Vázquez, F. J., López-Olmeda, J. F., & Vera, L. M. (2024). Effect of light and feeding regimes on the daily rhythm of thermal preference in Nile tilapia (Oreochromis niloticus). Aquaculture, 578, 740122. https://doi.org/10.1016/J.AQUACULTURE.2023.740122

Del Pozo, A., Sánchez-Férez, J. A., & Sánchez-Vázquez, F. J. (2011). Circadian Rhythms of Self-feeding and Locomotor Activity in Zebrafish (Danio Rerio). Chronobiology International, 28(1), 39–47. https://doi.org/10.3109/07420528.2010.530728

Eneh, A. H., Udanor, C. N., Ossai, N. I., Aneke, S. O., Ugwoke, P. O., Obayi, A. A., Ugwuishiwu, C. H., & Okereke, G. E. (2023). Towards an improved internet of things sensors data quality for a smart aquaponics system yield prediction. MethodsX, 11, 102436. https://doi.org/10.1016/J.MEX.2023.102436

Hoess, R., Generali, K. A., Kuhn, J., & Geist, J. (2022). Impact of Fish Ponds on Stream Hydrology and Temperature Regime in the Context of Freshwater Pearl Mussel Conservation. Water (Switzerland), 14(16), 2490. https://doi.org/10.3390/W14162490/S1

Kyaw, T. Y., & Ng, A. K. (2017). Smart Aquaponics System for Urban Farming. Energy Procedia, 143, 342–347. https://doi.org/10.1016/J.EGYPRO.2017.12.694

Le Roy, A., Mazué, G. P. F., Metcalfe, N. B., & Seebacher, F. (2021). Diet and temperature modify the relationship between energy use and ATP production to influence behavior in zebrafish (Danio rerio). Ecology and Evolution, 11(14), 9791–9803. https://doi.org/10.1002/ECE3.7806

Li, D., Wang, Z., Wu, S., Miao, Z., Du, L., & Duan, Y. (2020). Automatic recognition methods of fish feeding behavior in aquaculture: A review. Aquaculture, 528, 735508. https://doi.org/10.1016/J.AQUACULTURE.2020.735508

Måløy, H., Aamodt, A., & Misimi, E. (2019). A spatio-temporal recurrent network for salmon feeding action recognition from underwater videos in aquaculture. Computers and Electronics in Agriculture, 167, 105087. https://doi.org/10.1016/J.COMPAG.2019.105087

Mamatha, V., & Kavitha, J. C. (2023). Machine learning based crop growth management in greenhouse environment using hydroponics farming techniques. Measurement: Sensors, 25, 100665. https://doi.org/10.1016/J.MEASEN.2023.100665

Pylatiuk, C., Zhao, H., Gursky, E., Reischl, M., Peravali, R., Foulkes, N., & Loosli, F. (2019). DIY Automated Feeding and Motion Recording System for the Analysis of Fish Behavior. SLAS Technology, 24(4), 394–398. https://doi.org/10.1177/2472630319841412

Quagrainie, K. K., Flores, R. M. V., Kim, H. J., & McClain, V. (2018). Economic analysis of aquaponics and hydroponics production in the U.S. Midwest. Journal of Applied Aquaculture, 30(1), 1–14. https://doi.org/10.1080/10454438.2017.1414009

Rubin, A. M., & Seebacher, F. (2024). Feeding frequency does not interact with BPA exposure to influence metabolism or behaviour in zebrafish (Danio rerio). Physiology & Behavior, 273, 114403. https://doi.org/10.1016/J.PHYSBEH.2023.114403

Taha, M. F., ElMasry, G., Gouda, M., Zhou, L., Liang, N., Abdalla, A., Rousseau, D., & Qiu, Z. (2022). Recent Advances of Smart Systems and Internet of Things (IoT) for Aquaponics Automation: A Comprehensive Overview. Chemosensors 2022, Vol. 10, Page 303, 10(8), 303. https://doi.org/10.3390/CHEMOSENSORS10080303

Ubina, N., Cheng, S. C., Chang, C. C., & Chen, H. Y. (2021). Evaluating fish feeding intensity in aquaculture with convolutional neural networks. Aquacultural Engineering, 94, 102178. https://doi.org/10.1016/J.AQUAENG.2021.102178

Verma, A. K., Chandrakant, M. H., John, V. C., Peter, R. M., & John, I. E. (2023). Aquaponics as an integrated agri-aquaculture system (IAAS): Emerging trends and future prospects. Technological Forecasting and Social Change, 194, 122709. https://doi.org/10.1016/J.TECHFORE.2023.122709

Wan, S., Zhao, K., Lu, Z., Li, J., Lu, T., & Wang, H. (2022). A Modularized IoT Monitoring System with Edge-Computing for Aquaponics. Sensors 2022, Vol. 22, Page 9260, 22(23), 9260. https://doi.org/10.3390/S22239260

Xuelong, H., Wentao, Z., Xinting, Y., Dinghong, W., Liang, P., Yuhao, Z., Chao, Z., Xuelong, H., Wentao, Z., Xinting, Y., Dinghong, W., Liang, P., Yuhao, Z., & Chao, Z. (2023). Identification of feeding intensity in recirculating aquaculture fish using water quality-sound-vision fusion. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, Vol. 39, Issue 10, Pages: 141-150, 39(10), 141–150. https://doi.org/10.11975/J.ISSN.1002-6819.202302041

Yanes, A. R., Martinez, P., & Ahmad, R. (2020). Towards automated aquaponics: A review on monitoring, IoT, and smart systems. Journal of Cleaner Production, 263, 121571. https://doi.org/10.1016/J.JCLEPRO.2020.121571

Zhou, C., Lin, K., Xu, D., Chen, L., Guo, Q., Sun, C., & Yang, X. (2018). Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture. Computers and Electronics in Agriculture, 146, 114–124. https://doi.org/10.1016/J.COMPAG.2018.02.006




DOI: https://doi.org/10.26760/mindjournal.v9i2.235-247

Refbacks

  • Saat ini tidak ada refbacks.


____________________________________________________________

ISSN (cetak) : 2338-8323  |  ISSN (elektronik) :  2528-0902

diterbitkan oleh:

Informatika Institut Teknologi Nasional Bandung

Alamat : Gedung 2 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 181)  Fax. 7202892

Email : mind.journal@itenas.ac.id

____________________________________________________________

Statistik Pengunjung :

Flag Counter

  Web
Analytics Statistik Pengunjung

 Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License