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Paper ID: 90

AlexNet Convolutional Neural Network to Classify the Types of Indonesian Coffee Beans

Y Hendrawan1, B Rohmatulloh1, F I Ilmi1, M R Fauzy2, R Damayanti1, D F Al Riza1, M B Hermanto1 and Sandra1

1Laboratory of Mechatronics and Agro-industrial Machineries, Department of Agricultural Engineering, Universitas Brawijaya, Jl. Veteran, Malang, ZIP 65145, Indonesia

2Department of Industrial Engineering, Universitas Merdeka, Jl. Terusan Raya Dieng 62-64, Malang, ZIP 65146, Indonesia


Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans i.e. Gayo Aceh, Kintamani Bali, and Toraja Tongkonan using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp, as well as the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1040 iterations. This study also used 100 testing-set data on each type of coffee beans. In the testing confusion matrix, the accuracy reached 99.6%.

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