Comparative Evaluation of Convolutional Neural Network Full Learning Model with Transfer Learning (VGG-16) for Coffee Bean Roasting Level Classification

Mradipta Nindya  Tama; Amanat Bintang  Saptomo; Afrido; Dawi Karomati  Baroroh; Achmad Pratama Rifai; Nguyen Huu Tho

doi:10.59395/ijadis.v6i2.1358

Authors

Mradipta Nindya Tama Universitas Gadjah Mada
Amanat Bintang Saptomo Universitas Gadjah Mada
Afrido Universitas Gadjah Mada
Dawi Karomati Baroroh Universitas Gadjah Mada
Achmad Pratama Rifai Universitas Gadjah Mada
Nguyen Huu Tho Faculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

DOI:

https://doi.org/10.59395/ijadis.v6i2.1358

Keywords:

Machine Learning, CNN, Roasting, Classification, VGG-16, deep learning, neural network

Abstract

Indonesia is the 3rd largest coffee producing country in the world in 2022-2023 with coffee production reaching 11.85 million bags per 60 kg of coffee. One of the important processes in coffee production is roasting because the roasting level of coffee beans can affect the taste and aroma of coffee. The problem faced is that the process of assessing the level of coffee roasting is traditionally carried out through visual observation by an expert (roaster). This method produces a subjective level of assessment and requires high skills and experience, making the assessment of the level of coffee roasting less efficient and prone to human error. Therefore, in this study the author aims to develop a Convolutional Neural Network (CNN) model for the classification of the level of coffee bean roasting that can achieve better and faster accuracy. In this study, the author compared two CNN architecture approaches for the classification of the level of coffee bean roasting. The first approach is full learning with an architecture consisting of three convolution layers. The second approach is transfer learning based on the VGG-16 model. From the results of the analysis, it is known that the full learning model has a better level of accuracy and a faster running time than the VGG-16 transfer learning. The CNN full learning model for coffee bean roasting level classification is able to classify the coffee bean roasting level, with an accuracy of 98.75% and a running time of 856 ms per step. The application of CNN for coffee roasting level classification can provide benefits such as improving quality control and reducing the level of subjectivity of a roaster in assessing the roasting level of coffee beans.

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