Implementation of Gas and Sound Sensors on Temperature Control of Coffee Roaster Using Fuzzy Logic Method

The flavor quality of a coffee is determined by the roasting process of the coffee beans. In this study, a prototype of a coffee roaster was designed and realized. This system is equipped with temperature control based on fuzzy logic method which can be set for several roasting levels, namely light, medium and dark. This control system regulates the gas flow on the stove and the fan motor speed on the drum roaster. The light level is based on responses from the gas sensor, meanwhile, the medium and dark levels are based on the cracking sound of the coffee beans. The Fast Fourier Transform and the Neural Network pattern recognition are used to stop the roasting process for both levels. The experimental results show that there are significant differences in the response of the gas sensor for each roasting level. The Neural Network can distinguish between the cracking sounds of the coffee beans and the background noise based on their frequency spectrum. This system can produce coffee beans according to their roasting levels.