PENERAPAN DEEP LEARNING UNTUK KLASIFIKASI KESEGARAN DAGING SAPI BERBASIS MOBILE APPS
The staple food that is popular and sought after today is an animal protein derived from meat. The need for beef in Indonesia has always increased significantly from year to year, but this need is inversely proportional to the beef produced. This caused beef imports to soar and the selling price of...
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2022-12-06.
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| 100 | 1 | 0 | |a Teris Ekamila Wahyundari Putri, . |e author |
| 245 | 0 | 0 | |a PENERAPAN DEEP LEARNING UNTUK KLASIFIKASI KESEGARAN DAGING SAPI BERBASIS MOBILE APPS |
| 260 | |c 2022-12-06. | ||
| 500 | |a http://repository.upnvj.ac.id/22097/23/ABSTRAK.pdf | ||
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| 500 | |a http://repository.upnvj.ac.id/22097/12/ARTIKEL%20KI.pdf | ||
| 520 | |a The staple food that is popular and sought after today is an animal protein derived from meat. The need for beef in Indonesia has always increased significantly from year to year, but this need is inversely proportional to the beef produced. This caused beef imports to soar and the selling price of beef to go up as well. The increase in the price of beef has made many traders commit fraud by mixing fresh meat with meat that is not fresh. People still use the traditional way of choosing meat which is still less effective. So to overcome this problem, we need a system that can classify and detect the freshness of beef according to its characteristics. One of the deep learning methods that are widely used today is the Convolutional Neural Network (CNN). CNN is currently the best method in image processing and identifying an object with an image as input. The image is processed into a model that can classify classes on the freshness of beef. The best classification model in this study is with 100% accuracy on the training data and test data, the loss value is 0.0233 using the Learning Rate, epoch and optimizer parameters to increase the level of accuracy in the model. An implementation model for Android-based mobile apps that can be used to detect the freshness level of beef. | ||
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| 690 | |a TA Engineering (General). Civil engineering (General) | ||
| 690 | |a TK Electrical engineering. Electronics Nuclear engineering | ||
| 655 | 7 | |a Thesis |2 local | |
| 655 | 7 | |a NonPeerReviewed |2 local | |
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