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- Title
Computer vision for purity, phenol, and pH detection of Luwak Coffee green bean.
- Authors
Hendrawan, Yusuf; Widyaningtyas, Shinta; Sucipto, Sucipto
- Abstract
Computer vision as a non-invasive bio-sensing method provided opportunity to detect purity, total phenol, and pH in Luwak coffee green bean. This study aimed to obtain the best Artificial Neural Network (ANN) model to detect the percentage of purity, total phenol, and pH on Luwak coffee green bean by using color features (red-green-blue, gray, hue-saturation-value, hue-saturation-lightness, L*a*b*), and Haralick textural features with color co-occurrence matrix including entropy, energy, contrast, homogeneity, sum mean, variance, correlation, maximum probability, inverse difference moment, and cluster tendency. The best ANN structure was (5 inputs; 30 nodes in hidden layer 1; 40 nodes in hidden layer 2; and 3 outputs) which had training mean square error (MSE) of 0.0085 and validation MSE of 0.0442.
- Subjects
GREEN bean; COFFEE beans; COMPUTER vision; PHENOL; ARTIFICIAL neural networks
- Publication
Telkomnika, 2019, Vol 17, Issue 6, p3073
- ISSN
1693-6930
- Publication type
Article
- DOI
10.12928/TELKOMNIKA.v17i6.12689