Comparison methods of Fuzzy Logic Control and Feed Forward Neural Network in automatic operating temperature and humidity control system (Oyster Mushroom Farm House) using microcontroller

The productivity of oyster mushroom cultivation in low-lying areas are still not optimal. This is due to the cultivation of oyster mushrooms needs ideal temperature and humidity (temperature 22–28 ° C with a humidity of 60% – 80%), while nowadays temperature and humidity preservation process is done in a conventional manner. Given these problems, the researchers gave the solution by creating a tool that able to work automatically to monitor and control the temperature and humidity in oyster mushroom cultivation problem based on microcontroller. Inputs used in these system are the value of temperature and humidity data readings from DHT11. While the output of the system is two actuators, the first is the exhaust fan and the second is mist maker. In the operation of the appliance automatically there will be two choices of data processing methods are applied, the method of Fuzzy Logic Control (FLC) and Feed Forward Backpropagation Neural Network (BPNN). Performance tools based on the application of these two methods will be compared to determine the most optimal and effective method when it applied to the tool to automatically control temperature and humidity oyster mushroom farm house. Based on the test results and data analysis, the tools can work well and also perform that optimal and effective data processing method is Neural Network with an average conditioning response time of 69.8 seconds to reach the ideal temperature and 113.4 seconds for the ideal humidity.