PEMUTUAN EDAMAME MENGGUNAKAN PENGOLAHAN CITRA DAN JARINGAN SYARAF TIRUAN
The objective of this research was to develop a computer program of image processing and articial neural netwok to the quality of fresh soybean (edamame) into four classes namely SQ (standar quality), SG (scond grade), TG ( third grade), and RJ (reject) using image processing and artifical neural network. The total samples were 2500 fresh soybean produced by PT. Mitra Tani Dua Tujuh Jember. Soybean image was analyzed to get six quality parameters whose match with Soybean quality criteria namely pod length, pod area, perimeter, defect area, index of red color, and of the artifical neural network (AAN). Six variations of ANN weredeveloped for ANN training purposes (2000 data). The weights of the selected ANN architecture was used to identify the quality class of testing data (500 data). Thenintegrated with image processing program so the program could identify Soybean quality class automatically. The quality parameter used in this research has relevancy with Soybean quality criteria. The selected architecture of the AAN was the one with 20 nodes hidden layer in which normalization onput data representation with zero mean and standard deviation equals one. The accuracy of image processing program observed 81,4 percent based on the 500 testing data.
Keyword: Grading, Edamame, image procssing, Artificial neural network
Diterima: 17 Mei 2006; Disetujui: 13 Juni 2006
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