IMPLEMENTATION OF DEEP LEARNING ON FLOWER CLASSIFICATION USING CNN METHOD - Pratiwi, Anggun; Fauzi, Ahmad (Jurnal Teknik Informatika (Jutif), 2024)
Technological developments in the field of artificial intelligence, particularly deep learning, have made significant contributions to various applications, including pattern recognition and object classification in visual images. One of the interesting applications of deep learning is image classification, where these techniques have proven effective in tackling complex problems, such as object recognition in visual images. Flowering plants, with approximately 369,000 known species, are an interesting object of study. In an effort to classify different types of flowers quickly and efficiently, a digital approach is a must. This research aims to implement deep learning technology, especially CNN method, in flower classification. One method that can be used is Convolutional Neural Network (CNN), a deep learning algorithm that is able to process image information well. In flower type classification, supervised learning techniques are essential. By giving the label (flower type) to the algorithm as the basis of truth, the use of CNN on a large scale can produce predictions and classifications with a high level of accuracy. This research produces a classification model that is more precise and able to overcome variations in flower morphology with 2 different datasets namely Oxford17 resulting in 84% accuracy and oxford102 resulting in an accuracy value of 64%.