Optimization of the Application of the Convolution Neural Network Algorithm in the Classification of Beef Freshness Level
Optimasi Penerapan Algoritma Convolution Neural Network Dalam Klasifikasi Tingkat Kesegaran Daging Sapi
DOI:
https://doi.org/10.21070/ups.3808Keywords:
Beef, Convolution Neural Network, ClassificationAbstract
This journal discusses the optimization of the application of the Convolutional Neural Network (CNN) algorithm to overcome the issue of mixing fresh and non-fresh beef on the market. The focus of the research is classification of freshness levels in beef images using the CNN method with the ADAM optimizer. The results of the study show that this method is very effective in identifying and classifying the level of freshness in beef images. By determining optimal parameters, the model achieved the highest level of accuracy of 98.50% at 10 epochs and a learning rate value of 0.001. The conclusion of this research is that the application of CNN with ADAM optimizer provides an effective solution to the problem of beef freshness level classification. This model can be implemented in various applications or other solutions, providing support to stakeholders who care about the quality and integrity of beef in the market.
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