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Deep Learning to Detect and Classify the Purity Level of Luwak Coffee Green Beans

Yusuf Hendrawan, Shinta Widyaningtyas, Muchammad Riza Fauzy, Sucipto Sucipto, Retno Damayanti, Dimas Firmanda Al Riza, Mochamad Bagus Hermanto and Sandra Sandra

Pertanika Journal of Science & Technology, Volume 30, Issue 1, January 2022

DOI: https://doi.org/10.47836/pjst.30.1.01

Keywords: Classification, convolutional neural network, Luwak coffee green beans, purity

Published on: 10 January 2022

Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world. In order to lower production costs, Indonesian producers and retailers often mix high-priced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method’s development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, ResNet-50, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best CNN model using ResNet-50 with optimizer type RMSProp and learning rate 0.0001, providing an accuracy average of up to 85.00%. Later, the CNN model can be used to establish a real-time, non-destructive, rapid, and precise purity detection system.

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ISSN 0128-7702

e-ISSN 2231-8534

Article ID

JST-2536-2021

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