Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures
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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Udayana University, Institute for Research and Community Services
2024-07-01
|
| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/108844 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832575508645150720 |
|---|---|
| author | Ni Luh Gede Pivin Suwirmayanti I Wayan Budi Sentana I Ketut Gede Darma Putra Made Sudarma I Made Sukarsa Komang Budiarta |
| author_facet | Ni Luh Gede Pivin Suwirmayanti I Wayan Budi Sentana I Ketut Gede Darma Putra Made Sudarma I Made Sukarsa Komang Budiarta |
| author_sort | Ni Luh Gede Pivin Suwirmayanti |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4352d04960c342fba59631b3881627a4 |
| institution | Kabale University |
| issn | 2088-1541 2541-5832 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj-art-4352d04960c342fba59631b3881627a42025-01-31T23:56:26ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322024-07-011502879810.24843/LKJITI.2024.v15.i02.p02108844Deep Learning Implementation Using CNN to Classify Bali God Sculpture PicturesNi Luh Gede Pivin SuwirmayantiI Wayan Budi SentanaI Ketut Gede Darma PutraMade SudarmaI Made SukarsaKomang BudiartaEfforts 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.https://ojs.unud.ac.id/index.php/lontar/article/view/108844 |
| spellingShingle | Ni Luh Gede Pivin Suwirmayanti I Wayan Budi Sentana I Ketut Gede Darma Putra Made Sudarma I Made Sukarsa Komang Budiarta Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures Lontar Komputer |
| title | Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures |
| title_full | Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures |
| title_fullStr | Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures |
| title_full_unstemmed | Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures |
| title_short | Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures |
| title_sort | deep learning implementation using cnn to classify bali god sculpture pictures |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/108844 |
| work_keys_str_mv | AT niluhgedepivinsuwirmayanti deeplearningimplementationusingcnntoclassifybaligodsculpturepictures AT iwayanbudisentana deeplearningimplementationusingcnntoclassifybaligodsculpturepictures AT iketutgededarmaputra deeplearningimplementationusingcnntoclassifybaligodsculpturepictures AT madesudarma deeplearningimplementationusingcnntoclassifybaligodsculpturepictures AT imadesukarsa deeplearningimplementationusingcnntoclassifybaligodsculpturepictures AT komangbudiarta deeplearningimplementationusingcnntoclassifybaligodsculpturepictures |