AlexNet convolutional neural network to classify the types of Indonesian coffee beans

Autor:	Sandra, M R Fauzy, Yusuf Hendrawan, Retno Damayanti, D. F. Al Riza, B Rohmatulloh, Mochamad Bagus Hermanto, F I Ilmi
Rok vydání:	2021
Předmět:	Indonesian business.industry Computer science language Pattern recognition General Medicine General Chemistry Artificial intelligence business Convolutional neural network language.human_language
Zdroj:	IOP Conference Series: Earth and Environmental Science. 905:012059
ISSN:	1755-1315 1755-1307
DOI:	10.1088/1755-1315/905/1/012059
Popis:	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 and 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 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0bf60c980fa7827f9e1e155f98ba2bb2 https://doi.org/10.1088/1755-1315/905/1/012059 Zobrazit plný text záznamu