<![CDATA[Abstrac ; Some Indonesian people utilize food sources evenly. Tomatoes are known to have very good nutritional content so people can consume them every day. Many species/types of tomatoes have high similarity so it is difficult to distinguish them. Tomato fruit type recognition in this study employs Convolutional Neural Network. The stages of the method used are feature learning and classification. To classify tomato fruit types, the CNN network is trained with image training data. The training process is carried out by looking for a form of model that is following the data to be processed to get the best results. It is also used in the argumentation process on training and validation data so that overfitting does not occur in the CNN network. The experimental results show that the convolutional Neural Network method can recognize tomato types with an accuracy rate of 96.6%, recall of 100%, precision of 96.6%, and an F-1 Score of 96.28% of 30 images using Confusion Matrix testing. Keywords: classification, tomato fruit type, Convolutional Neural Network]]>
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