Literature Review of Accuracy Analysis in Digital Image Classification Using Deep Learning
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Abstract
The development of technology, particularly the advancement of Artificial Intelligence (AI), including machine learning, presents significant challenges in the field of research, especially in digital image classification using machine learning techniques. Digital image classification is a process based on the detection and identification of objects within images across various object categories. Several machine learning methods commonly used for digital image classification include K-Nearest Neighbor (KNN), Gray Level Co-occurrence Matrix (GLCM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). This study focuses on analyzing the accuracy level of one of the most widely used digital image classification methods, namely the Convolutional Neural Network (CNN). The analysis of CNN accuracy in digital image classification across various application domains is conducted based on a literature review of 40 journal articles related to digital image classification. The accuracy achieved in digital image classification is strongly influenced by the classification method, dataset characteristics, supporting computational tools, and architectural modifications applied to specific methods. In addition, the quality of image data used for both training and testing significantly affects the resulting accuracy. Therefore, performing data preprocessing or image enhancement on training and testing datasets can improve classification accuracy during evaluation. The results indicate that the average accuracy achieved using CNN methods outperforms Support Vector Machine (SVM), suggesting that the application of Convolutional Neural Networks in digital image classification across various objects demonstrates a relatively high level of accuracy.
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