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All Journal Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Journal of Tropical AgriFood
Pakaya, Isran Mohamad
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Agriculture, Biological Sciences & Forestry Engineering

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Classification of Roasting Level of Coffee Beans Using Convolutional Neural Network with MobileNet Architecture for Android Implementation Pakaya, Isran Mohamad; Radi, Radi; Purwantana, Bambang
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 3 (2024): September 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i3.924-932

Abstract

The roasting process has a significant impact on the aroma profile and taste of coffee making it an essential stage in the coffee processing. Currently, the classification of coffee bean roasting levels still relies on subjective human visual assessment, which can lead to errors due to fatigue or negligence. To overcome this problem, a classification system was developed using computer vision technology with a deep learning approach. The present study designed a coffee bean roasting level classification system based on image analysis integrated within an Android application. The Convolutional Neural Network (CNN) model with the MobileNet architecture was used to identify and classify coffee beans based on their roasting level. Two CNN models, namely CNN Alpha and CNN Beta were used in this study. The dataset included 1.600 coffee bean images, with 1.200 images used to train the model and 400 images used to test the accuracy. In this experiment, the input image had an optimal size of 70x70 pixels, a learning rate of 0.0001, and 100 epochs for both models. The model training and testing results in the highest accuracy of 98-88% in 6.40-0.0012 minutes.The application test results obtained 93.55% accuracy, 97.06% precision, and 96.67% recall. These results indicate that this model and application function optimally in classifying coffee bean roasting levels accurately. Overall, this study reveals the potential of integrating CNN with the MobileNet architecture into an Android-based application to change the way of roasting level classification, as well as to improve efficiency and accuracy. Keywords: Coffee, Roasting, Convolutional Neural Network, MobileNet, Android.
Development of Android application for coffee roast level classification using CNN mobilenetev3 based on digital image analysis Pakaya, Isran Mohamad; Radi, Radi; Dharmawan, Agus; Nurmaisari, Melda
Journal of Tropical AgriFood Volume 8 Nomor 3 Tahun 2026
Publisher : Department of Agricultural Products Technology, Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35941/jtaf.8.3.2026.26928.202-212

Abstract

Coffee is one of Indonesia's key commodities with high economic value in both domestic and international markets. Roasting is a crucial stage in coffee processing, as it directly influences the final taste and aroma. However, until now, the roasting level has been determined visually and subjectively, making it prone to human error. This study aimed to develop an automatic classification system for coffee roasting levels using a Convolutional Neural Network (CNN) approach integrated into an Android application. The CNN model was built using the MobileNetV3 architecture and applied with a transfer learning method on Google Colab. The dataset consisted of 1,600 coffee images with three roasting levels: light, medium, and dark. The trained model was then converted into TensorFlow Lite (TFLite) format for integration into an Android application called RoastScan. This app features automatic classification through the camera or gallery and provides real-time predictions with confidence levels. The evaluation results show that the model has a classification accuracy of 97% on the test data, with high precision, recall, and F1-score across all classes. Further testing via the application showed that the best accuracy reached 93.55%. These findings suggest that integrating CNN with mobile applications has the potential to be a practical, efficient, and accurate solution for determining coffee roasting degrees, as well as supporting the standardization of coffee product quality at both industrial and MSME levels.
Co-Authors Agus Dharmawan Bambang Purwantana Nurmaisari, Melda Radi Radi