Hyperparameter Model Architecture Xception in Classifying Zophobas Morio and Tenebrio Molitor - Amri Ismail Tumanggor; Muhathir Muhathir (Universitas Muhammadiyah Sumatera Utara, Indonesia, 2023)
Zophobas Morio and Tenebrio Molitor are popular larvae as feed ingredients that are widely used by animal lovers to feed reptiles, birds, and other poultry. However, these two larvae are similar in appearance; their nutritional content is very different. Zophobas Morio is more nutritious and has a higher economic value compared to Tenebrio Molitor. Due to limited knowledge, many animal lovers have difficulty distinguishing between the two. This study aims to build the best configuration of the Xception architecture hyperparameter model that can distinguish between the two. The model is trained using images taken from mobile phones. Training is carried out using the parameters Epoch 15, Batch Size 32, Optimizer Adam, RMSprop, and SGD. The experimental results on the dataset show that the best accuracy for the Xception architecture hyperparameter model is Optimizer Adam with an accuracy rate of 100%, and Optimizer SGD with an accuracy rate of 100%. And of course, it gives very good results