Indonesia ancient temple classification using convolutional neural network
Kefin Pudi Danukusumo, Pranowo Pranowo, Martinus Maslim · 2017
This paper describes the use of convolutional neural network(CNN) method to classify various image and photo of Indonesia ancient temple. The method itself implements Deep Learning technique designed for Computer Vision task. The idea behind CNN is image pre-processing through a stack of convolution layers to create many patterns that can be easily recognized. The result shows that the learning model has an accuracy of 98,99% on the training set and accuracy of 85.57% on the test set. With GPU performance, the time used to train the model is 389.14 seconds.