<![CDATA[Fruit type classification plays an important role in supporting the efficiency of distribution, sorting, and stock management processes in the agriculture and food industry. The use of technology in various aspects of life is growing rapidly, including in agriculture and agro-processing. Fruit type classification is an important stage in the fruit supply chain, starting from farmers to consumers. Traditionally, fruit type classification is done manually by human labor, which can be error-prone and time-consuming. With the advancement of technology, especially the development of Convolutional Neural Network (CNN) in deep learning, there is an opportunity to automate and improve the accuracy of the fruit type classification process based on images. Convolutional Neural Network (CNN) is one of the methods in deep learning that has proven effective in image processing and pattern recognition. This method has provided impressive results in various applications, including object classification in images. The purpose of this research is to find out how the architecture and results of the Convolutional Neural Networks (CNN) algorithm for image classification of fruit types. The method used is CNN with different epoch values on each training data. Training data is 9000 and testing data is 100, and validation data is 1000 data. The results obtained quite high accuracy training which reached 82.42% and accuracy validation reached 87.50%. from these results it can be concluded that the model is included in good accuracy and succeeded in identifying types of fruit when testing with test data.]]>
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