Klasifikasi Jenis Tanaman Rempah Rhizoma Zingiberaceae dengan Metode CNN dan VGG 19 - Haris Abdullah Firmasnsyah; Kurnaiwan Muchamad; Citra Nurina Prabiantissa; Syahri Muharom (Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh, 2024)
The need for identification of spice plant species is very important to achieve accuracy levels accurately and efficiently. Previous researchers have demonstrated the success of this CNN method in classifying various spice plant species. However, only three species of Zingiberaceae (also known as ginger) spice plants were studied in this research: ginger, turmeric, and galangal. There has not been much previous research on these plant species. To ensure label accuracy, this study compares the performance of two popular CNN optimizers, Adam and SGD. A dataset of spice plant images obtained from Internet websites was then diagnosed by experts. To prepare for training the CNN model with VGG19, the image data is pre-processed. The pre-trained VGG19 architecture is used as the basis for spice plant classification. The classification accuracy is used to evaluate the performance of the model. The results of the study show that in the classification of spice plants, the use of the pre-trained VGG19 architecture is used, providing research results that also show that the architectural CNN method successfully classifies Zingiberaceae spice plant species. Consistently, the use of Adam's optimizer resulted in higher accuracy than SGD. This suggests that Adam's optimizer may be more effective in optimizing VGG19 model parameters for spice plant classification