Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods


  • Rizka Zulfani Syahrir Student
  • Eri Prasetyo Wibowo Universitas Gunadarma



Leaves, Supervised Learning, Convolutional Neural Network, Classification, Accuracy


One part of the tree, namely the leaves, which grow on the branches, has several types of leaves consisting of 4 shapes, ranging from circular shapes, elongated shapes, and some even have a finger shape. Often we mistake the shapes of these leaves. This study discusses the classification of leaves based the shape of the leaf bones using the Convolutional Neural Network, which is used to classify data that has been labeled using one of the methods, namely supervised learning. The purpose of this method is to classify a variable into the variables that have been listed. The goal is to classify leaves based on leaf shape to implement a Convolutional Neural Network algorithm model for leaf classification based on bone shape, which will produce an accuracy value. Accuracy values are obtained from conducting experiments at the training and trial stages. So it can be concluded using the epochs parameter of 30 and a batch size of 128, using ReLU and Softmax activations. The results obtained for the accuracy value for training are 98.52%, while the validation is 89.06%.


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How to Cite

Syahrir, R. Z., & Eri Prasetyo Wibowo. (2021). Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 3(1), 1–7.