Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures

Ni Luh Gede Pivin Suwirmayanti
I Wayan Budi Sentana
I Ketut Gede Darma Putra
Made Sudarma (Department of Electrical Engineering, Faculty of Engineering, Udayana University)
I Made Sukarsa (Department of Information Technology, Faculty of Engineering, Udayana University)
Komang Budiarta

Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Vol 15 No 02 (2024): Vol. 15, No. 2 August 2024

Efforts to preserve Balinese culture can be carried out by integrating art and technology as new steps that need to be developed. This research is motivated by the existence of various forms of God statues which have a central role in Balinese culture. The Deep Learning method is proposed because it has unique features that can be extracted automatically. The technique used in Deep Learning is Convolutional Neural Network (CNN). The training process is first performed to perform the classification process, and then the testing process is performed. We compared our CNN model with two other models, AlexNet and ResNet, based on the experimental results. Using a data split of 70%- 30%, our CNN model has the highest accuracy in managing statue image data. Specifically, our model achieves 97.14% accuracy, while Alexnet and Resnet achieve 24.44% and 33.33%, respectively. Apart from contributing to introducing the Balinese God Statue, this research can also be a basis for developing more comprehensive applications in culture and tourism.