An IoT-Based Smart Feeding System for Koi Fish Using Mamdani Fuzzy Logic
DOI:
https://doi.org/10.51903/jtie.v5i1.480Keywords:
Fuzzy Logic, Internet of Things, Koi FIsh, Smart FeedingAbstract
Koi feeding management requires precision in both feeding timing and feed quantity to maintain fish health and reduce mortality rates. Manual feeding practices are often inconsistent due to human limitations, leading to overfeeding or underfeeding. This study proposes an IoT-based smart feeding system for koi fish that integrates Mamdani fuzzy logic to determine adaptive feeding durations based on feed stock conditions. The system employs a NodeMCU ESP8266 microcontroller, an ultrasonic sensor for feed-level monitoring, a servo motor for feed dispensing, and the Blynk platform for real-time remote monitoring and control over the internet. Mamdani fuzzy inference is utilized to classify feed levels into linguistic variables (low, medium, and high) and generate appropriate feeding actions. Experimental results demonstrate that the proposed system operates reliably, with an average measurement error of 1.59%, indicating high accuracy in feed-level detection. The fuzzy logic controller effectively adjusts feeding duration according to feed availability, enabling consistent and controlled feeding schedules. The proposed system offers a practical and low-cost solution for intelligent koi fish feeding management and can be extended to broader applications in smart aquaculture systems.
References
Amarudin, A., Saputra, D. A., & Rubiyah, R. (2020). Design and Development of an Automatic Fish Feeding System Using a Microcontroller. Jurnal Ilmiah Mahasiswa Kendali Dan Listrik, 1(1), 7–13. https://doi.org/10.33365/jimel.v1i1.231
Andrian, K. N., Wihadmadyatami, H., Wijayanti, N., Karnati, S., & Haryanto, A. (2024). A Comprehensive Review of Current Practices, Challenges, and Future Perspectives in Koi Fish (Cyprinus Carpio var. Koi) Cultivation. Veterinary World, 17(8), 1846–1854. https://doi.org/10.14202/vetworld.2024.1846-1854
Belgacem, H., & Chihi, I. (2025). Toward Reliable and Intelligent Sensor Systems: A Comprehensive Study of Fault Diagnosis and Mitigation. IEEE Sensors Reviews, 2, 511–536. https://doi.org/10.1109/sr.2025.3601092
Choudhary, A., Mian, T., & Fatima, S. (2021). Convolutional Neural Network Based Bearing Fault Diagnosis of Rotating Machine Using Thermal Images. Measurement, 176, 109196. https://doi.org/10.1016/j.measurement.2021.109196
Daru, A. F., Susanto, S., & Adhiwibowo, W. (2024). Arowana Cultivation Water Quality Monitoring and Prediction Using Autoregressive Integrated Moving Average. International Journal of Reconfigurable and Embedded Systems, 13(3), 665–672. https://doi.org/10.11591/ijres.v13.i3.pp665-672
Firmansyah, M. P., Nashir, M. N., Rahmeisi, N., Augusta, P. S., & Arfriandi, A. (2026). Tinjauan Literatur Sistematis tentang Deteksi Anomali Berbasis Kecerdasan Buatan untuk Intrusi Jaringan pada IoT. Jurnal Ilmiah Sistem Informasi, 5(1), 71–83. https://doi.org/10.51903/eqne0j35
Ibrahim, S. M., Go, E.-M., & Iranda, J. (2024). Scalable and Secure IoT-Driven Vibration Monitoring: Advancing Predictive Maintenance in Industrial Systems. Journal of Technology Informatics and Engineering, 3(3), 370–381. https://doi.org/10.51903/jtie.v3i3.210
Kusrini, E., Cindelaras, S., & Prasetio, A. B. (2015). Development of Ornamental Fish Aquaculture Technology in Indonesia. Indonesian Aquaculture Journal, 10(2), 101–110. https://doi.org/10.15578/iaj.10.2.2015.101-110
Li, L., Zhang, Q., & Huang, D. (2020). A Review of Imaging Techniques for Plant Phenotyping. Sensors, 20(9), 2674. https://doi.org/10.3390/s20092674
Mase, E. M., Witi, F. W., & Bhae, B. Y. (2025). Optimalisasi Pemantauan Level Air dalam Bak Penampungan Air Menggunakan Internet of Things (IOT) di Universitas Flores. Jurnal Ilmiah Sistem Informasi, 4(1), 163–179. https://doi.org/10.51903/4xg62v92
Mohamed, A. A., Muhammad, N. A. B., Rashid, R. A., Ahmed, M. M., Ali, A. A., & Abdikadir, N. M. (2024). IOT-Based Automatic Fish Feeding System. 2024 IEEE 22nd Student Conference on Research and Development (SCOReD), 333–338. https://doi.org/10.1109/scored63310.2024.10861942
Mudholkar, P., Mudholkar, M., Jijaba, K. J., & Kalita, J. P. (2025). Smart Aquaculture: IoT, Cloud Computing, and AI for Sustainable Fisheries. Vascular and Endovascular Review, 8(17s), 383–391. https://doi.org/10.15420/ver.2024.67
Nagothu, S. K., Bindu Sri, P., Anitha, G., Vincent, S., & Kumar, O. P. (2025). Advancing Aquaculture: Fuzzy Logic-Based Water Quality Monitoring and Maintenance System for Precision Aquaculture. Aquaculture International, 33(1), 32. https://doi.org/10.1007/s10499-024-01532-7
Nanjar, A., Maharani, T. S., Prastyo, P. A., Hidayat, M. T. N., & Najibulloh, I. kharits. (2024). Internet of Things (IoT) Integration in Telecommunication Networks: Challenges and Opportunities. Journal of Technology Informatics and Engineering, 3(1), 11–24. https://doi.org/10.51903/jtie.v3i1.156
Noor, M. Z. H., Hussian, A. K., Saaid, M. F., Ali, M. S. A. M., & Zolkapli, M. (2012). The Design and Development of Automatic Fish Feeder System Using PIC Microcontroller. 2012 IEEE Control and System Graduate Research Colloquium, 343–347. https://doi.org/10.1109/csgrc.2012.6259132
Papini, N., Rugini, L., Cecconi, M., Scorzoni, A., Tarantino, A., & Placidi, P. (2025). Measurement-Based Model for Water Content Estimation in Sustainable Granular Materials Using an IoT Custom Device. IEEE Transactions on Instrumentation and Measurement, 74, 1-9. https://doi.org/10.1109/tim.2025.1234567
Prapti, D. R., Mohamed Shariff, A. R., Che Man, H., Ramli, N. M., Perumal, T., & Shariff, M. (2022). Internet of Things (IoT)-Based Aquaculture: An Overview of IoT Application on Water Quality Monitoring. Reviews in Aquaculture, 14(2), 979–992. https://doi.org/10.1111/raq.12675
Pribadi, W., Prasetyo, Y., & Juliando, D. E. (2020). Design of Fish Feeder Robot Based on Arduino-Android With Fuzzy Logic Controller. Int. Res. J. Adv. Eng. Sci, 5(4), 47–50. https://doi.org/10.21776/ub.irjaes.2020.005.04.07
Singh, R. K., Berkvens, R., & Weyn, M. (2021). AgriFusion: An Architecture for IoT and Emerging Technologies Based on a Precision Agriculture Survey. IEEE Access, 9, 136253–136283. https://doi.org/10.1109/access.2021.3118814
Susilo, B. W., & Susanto, E. (2024). Employing Artificial Intelligence in Management Information Systems to Improve Business Efficiency. Journal of Management and Informatics, 3(2), 212–229. https://doi.org/10.51903/jmi.v3i2.30
Sutabri, T., Octavianto, T., & Widodo, Y. B. (2021). Fuzzy Logic-Based Automatic Feeding System for Ornamental Fish Using IoT Technology. Journal of Telecommunication, Electronic and Computer Engineering, 13(2), 45–52. https://doi.org/10.11591/jtec.v13i2.25258
Tang, J., Alelyani, S., & Liu, H. (2014). Feature Selection for Classification: A Review. In H. Liu & H. Motoda (Eds.), Feature Selection for Data and Pattern Recognition, 37–64. https://doi.org/10.1007/978-3-642-41836-6_3
Tarihoran, A. D. B., Hubeis, M., Jahroh, S., & Zulbainarni, N. (2024). Building a Sustainable Institutional Model for Ornamental Fish Farming Export Villages in Indonesia. International Journal of Agricultural Sustainability, 22(1), 2401203. https://doi.org/10.1080/14735903.2024.2401203
Xu, P., Xu, H., Xiao, P., & Guo, Z. (2026). Multisensor Visual Cognition Framework-Supported Intelligent Feeding Approach for Industrial Aquaculture. IEEE Sensors Journal, 26(2), 2955–2969. https://doi.org/10.1109/jsen.2025.3156247
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
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