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All Journal JURNAL RELASI STIE MANDALA JEMBER JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengembangan Wiraswasta JABE (Journal of Applied Business and Economic) Community Development Journal: Jurnal Pengabdian Masyarakat FOCUS: Journal of Social Studies Jurnal Venus Relasi : Jurnal Ekonomi Kalao’s Maritime Journal
Rudy Susanto
Program Studi Pendidikan Ekonomi Universitas Indraprasta PGRI
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Visual Segmentation and Classification of Coffee Beans After Roasting Firdaus Alamanda; Rudy Susanto; Wiji Lestari
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9758

Abstract

This research aims to develop an image-based system for segmenting and classifying coffee beans after roasting using deep learning. A U-Net architecture was applied to isolate coffee beans from the background with high spatial accuracy, achieving a mean Intersection over Union (IoU) of 0.8833 and Dice Coefficient of 0.9375. The segmented images were then classified into six roasting levels green, light, light to medium, medium, medium to dark, and dark using a modified ResNet-50 model, which reached an overall classification accuracy of 86%. The system demonstrates strong performance for clear categories but shows overlapping predictions for visually similar classes such as “medium” and its neighboring levels, indicating that boundaries between roasting stages can be ambiguous. This study provides an objective and automated alternative for roast quality inspection, reducing reliance on subjective human assessment. However, to meet industrial standards, further improvements are needed, such as integrating additional image features or ensemble models to increase discrimination power. This two-stage system serves as a promising foundation for future developments in automated coffee quality control.
Co-Authors Ai Annisa Utami Elis Silmi Fathur Nanda Saputra Firdaus Alamanda Hadi Setiawan Indah Pangesti Indah Pangesti Ismail Dwi Cahyo Khoirul Umam Mardiyati, Sri - Micheal Sonny Mohammad Romadona Mursalim Rahim Prasetyo Sutrisno, Slamet Siti Nurlaili Triwahyuni WIJI LESTARI Zahrudin zahrudin Zainal Arifin H. Masri