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All Journal Jurnal Mutu Pangan : Indonesian Journal of Food Quality
Putra, Dimas Zaki Alkani
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Agriculture, Biological Sciences & Forestry Chemistry Public Health

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Convolutional Neural Network for Ground Coffee Particle Size Classification Putra, Dimas Zaki Alkani; Rifai, Achmad Pratama
Jurnal Mutu Pangan : Indonesian Journal of Food Quality Vol. 11 No. 1 (2024): Jurnal Mutu Pangan
Publisher : Department of Food Science and Technology (ITP), Faculty of Agricultural Technology, Bogor Agricultural University (IPB) in collaboration with the Indonesian Food and Beverage Association (GAPMMI), the National Agency of Drug and Food Control, and th

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jmpi.2024.11.1.36

Abstract

Indonesia is the fourth largest coffee-producing country in the world. The popularity of coffee is increasing due to people's curiosity about the origin of coffee, from harvest to the hot cup of coffee on their table. This coffee culture drives innovators to develop coffee processing technology. Currently, there are tens of different coffee brewing methods available, each with their own unique flavor characteristics. The particle size of coffee beans is the basis for brewing coffee using specific methods. Identifying the particle size and calibrating tools to grind coffee requires special skills, expertise, experience, and a time-consuming process. Therefore, this study aims to develop a tool to classify the particle size of ground coffee based on computer vision. The object of this research is ground coffee with various particle sizes, which are acquired through imagery and will be classified using Convolutional Neural Network to provide recommendations for brewing coffee according to the particle size of the ground coffee. To build the classification model, the architectures were trained by full learning and transfer learning using VGG-19, MobileNet, and InceptionV3. The results showed that the classification model using the Convolutional Neural Network using the cellphone camera dataset achieved an accuracy value of 0.80. Meanwhile, with the microscope dataset, the model's accuracy only reached 0.58. Therefore, the classification model using the cellphone dataset is feasible to be implemented to determine the particle size.
Co-Authors Achmad Pratama Rifai