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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Satrio Adi Priyambada
National Research and Innovation Agency
Computer Science & IT Electrical & Electronics Engineering

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Performance Comparison of CNN Transfer Learning Models for Coffee Bean Quality Classification Nur Muhammad Fadli; Prawidya Destarianto; Hendra Yufit Riskiawan; Bekti Maryuni Susanto; Satrio Adi Priyambada; Wawan Hendriawan Nur; Mukhamad Angga Gumilang
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.457

Abstract

According to SNI Standard No. 01-2907-2008, accurate sorting of coffee beans is crucial for improving export value. Manual sorting is time-consuming, subjective, and error-prone, especially when visual differences are subtle between roast levels. This study proposes and evaluates an automatic, machine-learning based system to support quality assurance in coffee production. We compare three transfer-learning CNN architectures: Xception, MobileNetV2, and EfficientNet-B1 on a publicly available dataset of 1,600 coffee bean images divided into four classes (dark, medium, light, green). All models were trained with the same preprocessing and hyperparameter settings. EfficientNet-B1 achieved the highest test accuracy (100%), followed by Xception (99.5%) and MobileNetV2 (97%). We discuss trade-offs between accuracy and computational efficiency and recommend EfficientNet-B1 for high-accuracy applications and MobileNetV2 for edge/mobile deployment.
Co-Authors Bekti Maryuni Susanto Hendra Yufit Riskiawan Mukhamad Angga Gumilang Nur Muhammad Fadli Prawidya Destarianto Wawan Hendriawan Nur