SUMMARY
The fruit is the part of the plant that is embedded in the soil so that it grows large, fleshy and has a lot of water content. There are about 295,383 species of seed plants that can produce fruit. By utilizing artificial intelligence, especially deep learning, it will make it easier to classify fruit. In this study, researchers used the Convolutional Neural Network (CNN) algorithm with the MobileNetV1 architecture to produce a fruit classification model. The data used is the fruit dataset from the Kaggle platform, namely the fruit 360 dataset. The data consists of 10 types of fruit (Avocado, Apple, Orange, Lemon, Lime, Mango, Pineapple, Banana, Watermelon and Strawberry) with 4729 training images and 1586 image testing image with a size of 100×100 pixels which has been converted to a size of 224×224 pixels. The stages of this research started with preparing fruit image datasets, preprocessing datasets, namely resizing images, modeling the Convolutional Neural Network (CNN) architecture using the MobileNetV1 architecture. The results of this study can classify fruit into 10 classes into a model and labels, producing a fruit classification model with 100% accuracy in model testing of training data and 100% accuracy in model testing of data testing.