Implementation of Digital Image to Determine the Quality of Bali Beef Using a Web-Based CNN Algorithm

Bali cattle are esteemed for their high-quality beef production; yet, the assessment of quality in practice sometimes lacks objectivity and consistency among farmers and traders. This subjectivity complicates purchasers' ability to ascertain equitable prices and heightens the danger of financial losses when the perceived quality diverges from the actual state of the meat. This paper offers an objective classification system for Bali beef quality via digital image processing via a Convolutional Neural Network (CNN) implemented with TensorFlow.js. A dataset including 600 training photos and 150 testing images was employed. Experimental results indicate that the created system attains an accuracy of 94.67% on training data and 78% on test data. The technique can classify beef quality into three categories: fresh, slightly fresh, and rotten. The results underscore the capability of web-based CNN models to deliver precise, accessible, and instantaneous evaluations of meat quality. In addition to technological performance, the deployment of this system can greatly reduce market asymmetry, facilitate transparent pricing methods, and diminish customer losses. Furthermore, it provides an economical digital solution applicable in rural or small-scale agricultural settings, thereby enhancing local agribusiness practices and fostering technological adoption in the livestock industry.