Perbandingan Kinerja Jaringan Saraf Tiruan Model Backpropagation dan General Regression Neural Network Untuk Mengidentifikasi Jenis Daging Sapi

*Nugroho Nugroho -  Magister Sistem Informasi, Universitas Diponegoro, Semarang
Eko Sediyono -  Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Salatiga
Suhartono Suhartono -  Magister Sistem Informasi, Universitas Diponegoro, Semarang
Published: 21 Apr 2011.
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Language: ID

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Abstract

The research on image identification has been conducted to identify the type of beef. The research is aimed to compare the performance of  artificial  neural  network  of  backpropagation  and  general  regression  neural  network  model  in  identifying  the  type  of  meat.  Image management is processed by counting R, G and B value in every meat image, and normalization process is then carried out by obtaining R, G, and B index value which is then converted from RGB model to HSI model to obtain the value of hue, saturation and intensity. The resulting value of image processing will be used as input parameter of training and validation programs. The performance of  G RNN model is more accurate than the backpropagation with accuracy ratio by 51%.

Keyword: Identification; Backpropagation; GRNN

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