Deteksi Tingkat Kematangan Fermentasi Singkong (Tape Singkong) Menggunakan Convolutional Neural Network (CNN)
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Abstract
Tapay is a food in which the manufacturing process involves yeast. Unlike others, tapay requires fermentation using yeast containing the Kapang Amylomyces Rousi, Mucor sp, Rhizopus sp, Khamir Saccharomycopsis fibuligera, Candida Utilis, Pichia burtonii, Saccharomyces Cerevisiae, Saccharomycopsis Malanga, and the bacteria Pediococcus sp and Bacillus sp. Cassava tapay (Manihot Utilissima) is food containing these elements. Problems arise when the common public has no idea about the ripeness of cassava fermentation. Therefore, an artificial neural system is developed to detect the ripeness of cassava fermentation using the Convolutional Neural Network (CNN) method. The CNN method is one of the Deep Learning methods that can carry out an independent learning process for object recognition that is extracted and classified, then can be applied to high-resolution images with a nonparametric distribution model. The study results by making 45 training data reached 96.88%, and using 30 cassava tapay test data reached 90%. These results aim to reduce community error, especially for consumers, in determining the ripeness of cassava tapay.
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References
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