Correlation Analysis of PWM Duty Cycle, Dispersal Distance, and Tilapia Growth Performance in an Automated Feeding System
Keywords:
fish feeder, internet of things, VL53L5CX sensor, aqua cultureAbstract
Aquaculture is an aquatic biota cultivation activity whose success is largely determined by water quality and feeding patterns. Water temperature outside the optimal range can disturb fish appetite, metabolism, and growth, while manual feeding on large multi-pond farms tends to be irregular, prone to feed excess, cannibalism, and increased production costs due to uneven feed distribution. This study designs and develops a fish feeder machine with an Internet of Things (IoT)-based multiparameter smart control system, analyzes its working mechanism, and evaluates its performance characteristics. An experimental method was used, consisting of sensor testing, feed-dispersal mechanism testing at PWM variations of 65–100%, and overall device testing on a biofloc-system tilapia pond in Gowa Regency for 10 days during the morning (07:00 WITA) and evening (17:00 WITA) sessions. The VL53L5CX sensor estimated feed availability with an average accuracy of 98.88% (testing error 0.33–1.25%). The feed-dispersal mechanism showed a direct linear relationship between PWM and dispersal distance (1.24 m at 65% PWM up to 1.95 m at 100% PWM) and an inverse relationship with dispersal time. The system automatically adjusted the feeding dose based on water temperature, with a timestamp-based scheduling deviation of less than 30 seconds per day. The 10-day field trial showed stable tilapia biomass growth, with an average daily growth rate of 3.6 grams/day under optimal temperature conditions. The prototype functions adaptively and responsively to changes in water temperature, with accuracy and reliability adequate to support efficient feed management in aquaculture.
References
[1] Abdullah, A. H., Saad, F. S. A., Sudin, S., Ahmad, Z. A., Ahmad, I., Bakar, N. A., Omar, S., Sulaiman, S. F., Mat, M. H. C., Umoruddin, N. A., & Johari, B. H. (2021). Development of aquaculture water quality real-time monitoring using multi-sensory system and internet of things. Journal of Physics Conference Series, 2107(1), 012011. https://doi.org/10.1088/1742-6596/2107/1/012011
[2] Barkah, M. T., & Luthfi, M. (2022). Strategi pengembangan usaha budidaya ikan hias koki (Carrasius auratus) di Desa Cimuning Kecamatan Mustika Jaya Kota Bekasi. Universitas Singaperbangsa Karawang.
[3] Effendi, I., & Mulyadi. (2012). Budidaya perikanan. Universitas Terbuka.
[4] Efendi, Y. (2018). Internet of Things (IoT) sistem pengendalian lampu menggunakan Raspberry Pi berbasis mobile. STMIK Amik Riau.
[5] Fahrurrozi, A., & Linayati. (2023). Pengaruh perbedaan persentase kebutuhan pakan terhadap pertumbuhan dan rasio konversi pakan pada benih ikan bandeng (Chanos chanos). Universitas Pekalongan.
[6] Gao, G., Xiao, K., & Chen, M. (2019). An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms. Computers and Electronics in Agriculture, 166, 105013. https://doi.org/10.1016/j.compag.2019.105013
[7] Hang, L., Ullah, I., & Kim, D. (2020). A secure fish farm platform based on blockchain for agriculture data integrity. Computers and Electronics in Agriculture, 170, 105251. https://doi.org/10.1016/j.compag.2020.105251
[8] Kelabora, D. M. (2010). Pengaruh suhu terhadap kelangsungan hidup dan pertumbuhan larva ikan mas (Cyprinus carpio). Universitas Riau.
[9] Preetham, K., Mallikarjun, B. C., Umesha, K., Mahesh, F. M., & Neethan, S. (2019). Aquaculture monitoring and control system: An IoT based approach. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2), 1167–1170.
[10] Rahman, N. (2023). Analisis perbandingan kinerja sensor suhu DS18B20, sensor suhu LM35, dan sensor suhu PT100 untuk sistem pengukuran kualitas air dengan metode kalibrasi EURAMET CG-13. UIN Syarif Hidayatullah.
[11] Su, X., et al. (2020). Factors affecting aquaculture water quality: physical, biochemical, and biological contamination indicators.
[12] Syukhron, I., et al. (2021). Penggunaan aplikasi Blynk untuk monitoring dan kontrol jarak jauh pada sistem kompos pintar berbasis IoT. Universitas Singaperbangsa Karawang.
