OPTIMASI PENERAPAN ALGORITMA CONVOLUTION NEURAL NETWORK DALAM KLASIFIKASI TINGKAT KESEGARAN DAGING SAPI - Sahera, Charisa Nur; Rahmawati, Yunianita; Dijaya, Rohman (Politeknik Bisnis Indonesia, 2024)
This research discusses optimizing the application of the Convolutional Neural Network (CNN) algorithm to overcome the problem 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 ADAM optimizer. Data collection involves open and private data. Image preprocessing is done with LabelEncoder and cv2. The research results 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 accuracy level of 98.50% at 10 epochs and a learning rate value of 0.001. Confusion matrix shows good results with a high number of True Positives. By applying CNN with the ADAM optimizer, it provides an effective solution to the problem of classifying beef freshness levels because this model is able to classify beef images well.