PERBANDINGAN METODE JARINGAN SYARAF TIRUAN BACKPROPAGATION DAN ALGORITMA PENCOCOKAN DALAM MENGIDENTIFIKASI KEMATANGAN TOMAT BUAH BERDASARKAN CIRI WARNA RGB

RIZKI TUNJUNG SARI , NIM. 09650006 (2013) PERBANDINGAN METODE JARINGAN SYARAF TIRUAN BACKPROPAGATION DAN ALGORITMA PENCOCOKAN DALAM MENGIDENTIFIKASI KEMATANGAN TOMAT BUAH BERDASARKAN CIRI WARNA RGB. Skripsi thesis, UIN SUNAN KALIJAGA.

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Abstract

Research on the maturity of tomatoes recognition has been done by various methods and produce different accuracy. Method with a high degree of accuracy are implemented into a system. Therefore, a prototype is needed to compare several methods in order to obtain a good degree of accuracy. To identify the maturity of granola tomatoes, 63 tomatoes is captured using digital camera and stored in computer and in .jpg extension sized 680x680. The images are extracted the RGB values that becomes the main feature for the maturity of granola tomatoes identification. Identification is using two methods, Backpropagation Neural Network (BPNN) and Matching Algorithm for comparing the results of the accuracy of both methods. Matching algorithm that used is City Block, Euclidean, order 3 and 4 of Minkowski. Using 63 images, it shows the results of testing the accuracy of City Block, Euclidean, order 3 and 4 of Minkowski are 99,2 % and Euclidean has smallest distance than others. Testing with BPNN shows that the result is also 99,2 %. Optimal architecture is input with 3 nodes, 1 hidden layer with 3 nodes, and output layer with 2 nodes. The optimal parameters is MSE of 0, epoch of 1000, learning rate of 0,01, and mc of 0,6. But order 4 of Minkowski has the smallest running time than BPNN then order 4 of Minkowski becomes the best method to indentify the maturity of granola tomatoes.

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