Image Classification of Mangoes Using CNN VGG16 and AlexNet


  • Aldo Leviko Marshal Bina Nusantara University
  • Ahmad Nurul Fajar Bina Nusantara University



Mango is one of the fruits that is often consumed by Indonesian people and is the fruit with the third largest amount of production in Indonesia, but currently, there are obstacles in the export of mangoes in Indonesia where fruit conditions and regulations weaken the mango export process in Indonesia. This study focuses on comparisons and classifications in the use of the CNN VGG16 and AlexNet architectures for classifying arum manis mango images. In this research, 4 images of Arum Manis mangoes will be used with a dataset of 400. With the results of the classification carried out with the two architectural models, CNN VGG16 provides a high accuracy of 92.50% with the use of epoch 50 while the use of AlexNet gets an accuracy of 79.64%. This research was conducted to provide a solution to overcome the quality of mangoes that are not in accordance with the production standards of mangoes to be exported.