Perbandingan Kinerja Activation Function pada Algoritma Resnet untuk Klasifikasi Varietas Beras

Ayumi, Vina

JSAI (Journal Scientific and Applied Informatics)
Vol 7 No 2 (2024): Juni

Quality checking of rice seed varieties (Oryza sativa) is an important procedure for quality assessment in the agricultural sector. The application of transfer learning algorithms has shown good results in image recognition tasks, so this algorithm is suitable for classifying rice variety images automatically. The data classes to be analyzed are Arborio, Basmati, Ipsala, Jasmine and Karacadag based on morphological, shape and color features analysis using the ResNet algorithm. The experiment used three types of models, namely ResNet-TopHat-ReLU, ResNet-TopHat-LeakyReLU and ResNet-TopHat-eLU. The ResNet-TopHat-eLU model is the best model with training accuracy of 96.61%, validation accuracy of 95.12% and testing accuracy of 78.17%.