Implementasi Metode Convolutional Neural Network Untuk Identifikasi Citra Digital Daun

Authors

  • Asmaul Husnah Nasrullah Universitas Ichsan Gorontalo, Gorontalo
  • Haditsah Annur Universitas Ichsan Gorontalo, Gorontalo

DOI:

https://doi.org/10.30865/mib.v7i2.5962

Keywords:

Machine Learning, Deep Learning, ANN, CNN, Identification, Selection Features, Leaf Image, Confusion Matrix

Abstract

Convolutional Neural Network (CNN) is a deep learning algorithm that is widely used to identify and classify a digital image object. In this study the Convolutional Neural Network (CNN) is used as an algorithm that functions to identify leaf types (certain plants) based on images obtained from a public dataset provider named Daun Jamu Indonesia. The existence of image characteristics causes the assistance process to require a more detailed feature selection process. Therefore the CNN method is used in order to solve the problem. The Convolutional Neural Network (CNN) method is capable of performing image recognition by minimizing feature extraction. CNN is also reliable in processing unstructured data because it uses a multi-layered structure of artificial reasoning networks. The image recognition process is carried out by looking for the shape of the model that matches the processed data in order to get the best results. In this study, the augmentation process was carried out on the training data and validation data so that overfitting does not occur in the Convolutional Neural Network (CNN). The results obtained in this study indicate that the Convolutional Neural Network (CNN) method can identify leaf types with a measured accuracy rate of 92% using the Confusion Matrix evaluation method. It is hoped that this research can be used as a reference for the use of the Convolutional Neural Network (CNN) method for image data, especially plant leaf types.

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Published

2023-04-27

Issue

Vol. 7 No. 2 (2023): April 2023

Section

Articles

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