Deep Convolutional Neural Networks (CNNs) are established as a strong category of models for image classification issues. This study aims to use and analyses the accuracy of deep learning in classifying rips on oil fruit. This study trained dataset, deep convolutional neural network to classify 628 pictures into a pair of totally different categories. By victimization CNN with five epochs, the results show that the results provide promising leads to the classification with the accuracy of 95.6% that higher the compared previous strategies. This study was with success determination a picture classification by detected and differentiated the matureness of feather palm fruit.