Monitoring Kadar Amonia dalam Akuarium Ikan Menggunakan Metode Verifikasi Warna RGB dengan Memanfaatkan ESP32-CAM

Matius Bryant, Stephanus Antonius Ananda


The main problem that is often found is poor aquarium water quality during the maintenance period, which can result in an increase of ammonia levels in the aquarium. In this thesis, the specimen that will be used for testing is predatory fish, where the food of this fish is raw meat or live fish whose size is smaller than them. The leftover of this food can increase the production of ammonia in the aquarium water. An increase in ammonia production will result in an increase in the nitrogen cycle as well, where the cycle will produce more nitrogen which results in reduced oxygen in the water. The effect of ammonia on fish can vary from difficulty of breathing, loss of appetite, and over time it will cause death in fish. In this thesis, an IoT(Internet of Things)- based monitoring system for aquarium ammonia levels will be used. This problem has actually been handled in several previous studies, one of which was researched by Talanta, D. E. entitled "Arduino-based Design and Build of Arduino-based Ammonia & PH Water Control in Fish Cultivation", but was considered less successful because Talanta only used an MQ-155 sensor to detect ammonia gas. While in this thesis the automation system is used to monitor & control ammonia levels in the aquarium by using the Camera function of ESP32-CAM to take pictures of the test kit and will then be processed with Python by utilizing the OpenCV library to verify RGB color in order to determine the ammonia level in the water. Based on the results of the system testing that has been carried out, it can be concluded that the ammonia detection accuracy of this system is 66.7%, this is because the measurement range of the water test strips being used is quite large, ranging from 0-6 PPM. So, it cannot produce small results such as 0.25 PPM, because 0.25 PPM levels will be directly classified to 0.5 PPM levels. In addition, it can also be concluded from the experiment conducted for 4 days that the automatic water change system in this thesis has an accuracy rate of 87.5% (8 trials with 1 failure) in maintaining water parameters safe for fish.


Internet of Things; OpenCV; Exotic Fish; Monitoring; Automation; Mobile Application

Full Text:



Floyd, R. F., Watson, C., Petty, D., & Pouder, D. B. 2015.


, 2022, from



Luthfi, M. M. 2016, July 17. Mari Mengenal Apa itu Internet

of Thing (IoT). Retrieved January 30, 2022, from

Muh, R. 2018, October 2. Program Motor Servo. Retrieved

May, 2022 from

- motor-servo/

Nugroho, Muhammad Akbar. “Sistem Kontrol dan

Monitoring Kadar Amonia untuk Budidaya Ikan yang

Diimplementasi pada Raspberry Pi 3-B.”

JURNAL TEKNIK ITS Vol. 7, No. 2, 2018, pp. A374

Raspberry Pi Foundation. 2019. What is a Raspberry Pi?.

Retrieved 27 February, 2022, from,languages


Robert M. D., David M. C. and Martin W. B. 1997.

Ammonia in Fish Ponds. Southern Regional Aquaculture

Center (SRAC). Publication No. 463.

Suharda, R. 2016. Mengontrol Kadar Ammonia dalam

Budidaya Perikanan. Retrieved January 30, 2022, from


Talanta, Dimasanggie Elul. “Rancang Bangun Kontrol

Kadar Amonia dan PH air berbasis Arduino pada Budidaya


Otopro Volume 17 No. 1, Nov 2021, pp. 27-32,



  • There are currently no refbacks.

Jurnal telah terindeks oleh :