Convolutional network implementation for protected bird identification

Birds have so many kinds of species, and a bird's attractiveness depends on its shape, colour, and whether it singing. Unfortunately, there are 177 bird species in Indonesia that will disappear because of poaching and illegal trade. With good information from the bird's picture, it can help people stop trading vanished birds. There are 15 different species birds, including javan sparrow, javanese eagle, parrot king ambon, golden julang, green peacock, rainbow lorikeet, katsuri ternate, black head katsuri, king cockatoo, yellow-crested cockatoo, tanau parrot, black-winged white starling, sepah raja honey, javanese glasses, and white-crested cockatoo. Each species of bird contains 200 pictures, which are split into 98.7% for data training and 1.3% for data testing. This study uses a convolution neural network with Adam Optimizer and Nadam Optimizer for bird classification and uses a confusion matrix for evaluation. The CNN with AlexNet Architecture has 97,14% in accuracy training, 91.62% in accuracy validation, and 0.3 loss validation for the training model with 100 epochs, a learning rate of 0.001, and Adam Optimizer. According to the confusion matrix results show that bird in fifth class can be recognized with true 31 pictures form 39 pictures. The CNN with AlexNet Architecture has 97.99% in accuracy training, 90.60% in accuracy validation, and 0.38 loss validation for the training model with 500 epochs, a learning rate of 0.0001, and Nadam Optimizer. According to the confusion matrix results show that bird in seventh class can be recognized with true 31 pictures form 39 pictures