<![CDATA[The cattle industry worldwide faces a major threat from foot-and-mouth disease (FMD) due to the contagious nature of the virus. In Sumbawa Regency, FMD peaked in 2022 with 12,814 cases. Screening for FMD is very important for early detection and treatment. Currently, detection conducted by animal health officers is still manual, requiring 48 hours per animal to obtain diagnostic results and is prone to errors. The researchers aim to create a Prototype Automatic Detection System that includes an automatic foot-and-mouth disease (FMD) detection system application using App Designer (GUI) system-MATLAB to assist animal health officers in diagnosing animals infected with FMD, saving time and costs and saving animals. The proposed method for automatically extracting distinguishing features of cattle and classifying whether the cattle are sick or healthy utilizes the advantages of the Convolutional Neural Network (CNN) model. Based on the evaluation results of the developed system, the proposed system using the Convolutional Neural Network algorithm has better performance with an accuracy of 100% compared to the WEKA application, namely SMO with an accuracy of 90%, IBk (87%), Trees.J48 (86%), and Naive Bayes (79%). Therefore, highly efficient and accurate digital image processing techniques must be used to produce effective FMD disease screening. The proposed decision support system for clinical screening is expected to make a significant contribution and help reduce the workload of Animal Health Officers in detecting foot-and-mouth disease (FMD).]]>
