Fajrul Islamy, Kahlil Muchtar, Fitri Arnia, Rahmad Dawood, Alifya Febriana, Gregorius Natanael Elwirehardja, Bens Pardamean
Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia; Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, 11480, Indonesia; Computer Science Department, Master of Computer Science Program, BINUS Graduate Program, Bina Nusantara University, Jakarta, 11480, Indonesia
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