Analysis and design of pool water quality monitoring system bioflok engineering using Artificial Neural Network based on internet of things.

In the process of treating pond water using the biofloc technique for freshwater fish farming, the condition of the results of the treatment can cause the water quality in the freshwater fish culture pond to be unstable. It is because of the provision of feed and fish waste that causes the acidity (pH) changes in the pond water. Pond water with high acidity (pH) or alkaline cause the failure in fish farming. A good acidity (pH) for freshwater fish is 6.5 – 8. In addition to acidity (pH), another parameter that must be considered in the cultivation of freshwater fish using biofloc techniques is the normal intensive pond water temperature of 25 - 30°C. Abnormal water temperature conditions will result in the probability of survival of freshwater fish decreasing that affect the mortality rate of fish. Furthermore, monitoring of pond water in freshwater fish farming with biofloc techniques is still mostly done traditionally so it is not practical and the human error factor is quite high related to the level of accuracy of the measurement results. Therefore, the pH monitoring and water temperature is very important to be performed in freshwater fish farming with biofloc techniques. This study aimed to design a freshwater fish culture monitoring system technology with biofloc technique pond media management for pH and water temperature monitoring systems using an IoT-based on Artificial Neural Network (ANN) with engineering methods. This study used a pH sensor to measure the acidity of the water and a temperature sensor to measure the temperature in the water. Arduino UNO as a microcontroller was to send data that was processed using ANN to activate the water pump and display the results of measuring pH and water temperature in real time through the LCD and android application. The results of this study can be implemented as a pond water monitoring system design for freshwater fish farming using biofloc techniques and ANN analysis as data processing from sensors to result more precise estimates of pH and water temperature measurements. [ABSTRACT FROM AUTHOR]