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All Journal Horizon: Indonesian Journal of Multidisciplinary
Muhammad Alan Darma Saputra
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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Implementation of Coffee Bean Roasting Level Classification System Using CNN and Knn Models with Web-Based Real-Time Camera Andini Sintawati; Ira Windarti; Ari Rosemalatriasari; Muhammad Alan Darma Saputra
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i3.6545

Abstract

Manual coffee bean roasting assessment is still susceptible to operator subjectivity, variations in lighting conditions, and inconsistencies in results between assessors. This study aims to develop a real-time web-based coffee bean roasting classification system by integrating Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN) models. The study uses a quantitative experimental approach with a dataset of coffee bean images collected independently and expanded to 6,470 images, which are grouped into five classes: GreenRoasting, LightRoasting, MediumRoasting, DarkRoasting, and Unknown. All images are processed through a pre-processing stage including resizing to 160 × 160 pixels, normalization, data augmentation, and splitting training and test data with a ratio of 80:20 in stages. MobileNetV2 is used as a feature extractor in CNN, while KNN with a value of k = 7 and a cosine distance metric is applied for feature vector classification. The final prediction was obtained using a weighted ensemble method with a composition of 60% CNN and 40% KNN, then implemented in a Flask-based web application with support for real-time image upload and camera. Test results showed the ensemble model achieved an accuracy of 85.67% with an average response time of 1,247 ms. This system has the potential to support faster, more consistent, and objective coffee roasting level assessments, especially for small to medium-scale coffee businesses.
Co-Authors Andini Sintawati Ari Rosemalatriasari Ira Windarti