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Journal from gdlhub / 2020-01-09 14:45:59
Oleh : Yusuf Hendrawan, Shinta Widyaningtyas, Sucipto Sucipto, Telkomnika
Dibuat : 2020-01-09, dengan 1 file

Keyword : : artificial neural network, computer vision, Luwak coffee C
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/issue/view/640
Sumber pengambilan dokumen : web

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.

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