Open AccessArtificial Neural Network Application for Aroma Monitoring on The Coffe Beans Blending Process
Author(s) -
Roza Susanti,
Zas Ressy Aidha,
Milda Yuliza,
Suryadi Suryadi,
Surfa Yondri
Publication year - 2018
Publication title -
joiv : international journal on informatics visualization
Language(s) - English
Resource type - Journals
eISSN - 2549-9904
pISSN - 2549-9610
DOI - 10.30630/joiv.2.3.86
Subject(s) - backpropagation , aroma , artificial neural network , artificial intelligence , computer science , process (computing) , pattern recognition (psychology) , engineering , mathematics , food science , chemistry , operating system
This study aims to identify the type of coffee powder aroma from the coffee beans blending using backpropagation artificial neural network (ANN). Backpropagation is a controlled training implementing a weight adjustment pattern to achieve a minimum error value between the the predicted and the actual output. Within this study, the coffee aroma testing utilized electronic tasting sensor system consisted of 4 sensors namely TGS 2611, TGS 2620, TGS 2610 and TGS 2602. The coffee aroma monitoring and data collection in this system applied LabVIEW software as a virtual instrumentation. The testing result of this ANN was able to distinguish the coffee variety of Robusta, Arabica coffee powder and the one without any coffee aroma. The backpropagation architecture was formed by 3 layers consisting of 1 input layer with 4 input nerve cells, 1 hidden layer with 8 neural cells, and 2 output layers by applying the backpropagation training algorithm. The training data was taken from 70 data samples of each circumstance of coffee with 5 testing times. The results of the training and testing showed that the established backpropagation was capable to identify and differenciate the coffee powder in accordance with the given input with different average success rate; 91.96% for Robusta coffee, 100 % for Arabica coffee, and no 84.24% for without coffee aroma.