BibTex Citation Data :
@article{JSINBIS6040, author = {Nugroho Nugroho and Eko Sediyono and Suhartono Suhartono}, title = {Perbandingan Kinerja Jaringan Saraf Tiruan Model Backpropagation dan General Regression Neural Network Untuk Mengidentifikasi Jenis Daging Sapi}, journal = {Jurnal Sistem Informasi Bisnis}, volume = {1}, number = {1}, year = {2011}, keywords = {}, 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 }, issn = {2502-2377}, pages = {33--40} doi = {10.21456/vol1iss1pp33-40}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/06} }
Refworks Citation Data :
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
Note: This article has supplementary file(s).
Article Metrics:
Last update:
Last update: 2025-07-02 16:23:33
Authors who submit the manuscripts to Journal JSINBIS must understand and agree that if the manuscript is accepted for publication, the copyright of the article belongs to JSINBIS and Diponegoro University as the journal publisher.
Copyright includes the exclusive right to reproduce and provide articles in all forms and media, including reprints, photographs, microfilm and any other similar reproductions, as well as translations. The author reserves the rights to the following:
JSINBIS and Diponegoro University and the Editors make every effort to ensure that no false or misleading data, opinions or statements are published in this journal. The content of articles published in JSINBIS is the sole and exclusive responsibility of the respective authors.
Copyright transfer agreement can be found here: [Copyright transfer agreement in doc] and [Copyright transfer agreement in pdf].
JSINBIS (Jurnal Sistem Informasi Bisnis) is published by the Magister of Information Systems, Post Graduate School Diponegoro University. It has e-ISSN: 2502-2377 dan p-ISSN: 2088-3587 . This is a National Journal accredited SINTA 2 by RISTEK DIKTI No. 48a/KPT/2017.
Journal JSINBIS which can be accessed online by http://ejournal.undip.ac.id/index.php/jsinbis is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats
