IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON
Balinese ornament carving are a cultural heritage that is owned by especially the Balinese people. However, especially Balinese people only know the shape of the carving without knowing the name and characteristics of the Balinese traditional carving ornaments. Based on these problems, the research...
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| Format: | Article |
| Language: | English |
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Institut Bisnis dan Teknologi Indonesia
2021-04-01
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| Series: | SINTECH (Science and Information Technology) Journal |
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| Online Access: | https://ejournal.instiki.ac.id/index.php/sintechjournal/article/view/552 |
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| author | I Gede Rusdy Mahayana Putra Made Windu Antara Kesiman Gede Aditra Pradnyana I Made Dendi Maysanjaya |
| author_facet | I Gede Rusdy Mahayana Putra Made Windu Antara Kesiman Gede Aditra Pradnyana I Made Dendi Maysanjaya |
| author_sort | I Gede Rusdy Mahayana Putra |
| collection | DOAJ |
| description |
Balinese ornament carving are a cultural heritage that is owned by especially the Balinese people. However, especially Balinese people only know the shape of the carving without knowing the name and characteristics of the Balinese traditional carving ornaments. Based on these problems, the researchers have a solution to research about Balinese Ornament Carving Identification by utilizing digital image processing technology. In this study uses Gabor Filter as a feature extraction from the carved image that used and Multilayer Perceptron as a classifier. There are 18 (eighteen) classes of Balinese carving ornaments use in this study with a total of dataset is 268 (two hundred and sixty eight). The purpose of this study was to determine the level of identification accuracy of Balinese ornament carving with Multilayer Perceptron method. In the implementation using digital image processing technic with Multilayer Perceptron method was based on backpropagation learning algorithm with 10560 neuron input layers, 50 neuron hidden layers, and 18 neuron output layers as classifier obtained the accuracy for testing is 43%. Classification testing based on k-fold cross validation with K=5 results in average accuracy of 41.14% with optimum accuracy of 56% and accuracy testing with Confusion Matrix obtained the accuracy 43.3%, sensitivity 42.68% and specificity 96.87%.
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| format | Article |
| id | doaj-art-2cde2bf713404581a57aba56e1fb78b3 |
| institution | DOAJ |
| issn | 2598-7305 2598-9642 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | Institut Bisnis dan Teknologi Indonesia |
| record_format | Article |
| series | SINTECH (Science and Information Technology) Journal |
| spelling | doaj-art-2cde2bf713404581a57aba56e1fb78b32025-08-20T02:43:10ZengInstitut Bisnis dan Teknologi IndonesiaSINTECH (Science and Information Technology) Journal2598-73052598-96422021-04-014110.31598/sintechjournal.v4i1.552IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRONI Gede Rusdy Mahayana Putra0Made Windu Antara Kesiman1Gede Aditra Pradnyana2I Made Dendi Maysanjaya3Universitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan Ganesha Balinese ornament carving are a cultural heritage that is owned by especially the Balinese people. However, especially Balinese people only know the shape of the carving without knowing the name and characteristics of the Balinese traditional carving ornaments. Based on these problems, the researchers have a solution to research about Balinese Ornament Carving Identification by utilizing digital image processing technology. In this study uses Gabor Filter as a feature extraction from the carved image that used and Multilayer Perceptron as a classifier. There are 18 (eighteen) classes of Balinese carving ornaments use in this study with a total of dataset is 268 (two hundred and sixty eight). The purpose of this study was to determine the level of identification accuracy of Balinese ornament carving with Multilayer Perceptron method. In the implementation using digital image processing technic with Multilayer Perceptron method was based on backpropagation learning algorithm with 10560 neuron input layers, 50 neuron hidden layers, and 18 neuron output layers as classifier obtained the accuracy for testing is 43%. Classification testing based on k-fold cross validation with K=5 results in average accuracy of 41.14% with optimum accuracy of 56% and accuracy testing with Confusion Matrix obtained the accuracy 43.3%, sensitivity 42.68% and specificity 96.87%. https://ejournal.instiki.ac.id/index.php/sintechjournal/article/view/552Confusion MatrixGabor FilterIdentificationMultilayer Perceptron |
| spellingShingle | I Gede Rusdy Mahayana Putra Made Windu Antara Kesiman Gede Aditra Pradnyana I Made Dendi Maysanjaya IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON SINTECH (Science and Information Technology) Journal Confusion Matrix Gabor Filter Identification Multilayer Perceptron |
| title | IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON |
| title_full | IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON |
| title_fullStr | IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON |
| title_full_unstemmed | IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON |
| title_short | IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON |
| title_sort | identifikasi citra ukiran ornamen tradisional bali dengan metode multilayer perceptron |
| topic | Confusion Matrix Gabor Filter Identification Multilayer Perceptron |
| url | https://ejournal.instiki.ac.id/index.php/sintechjournal/article/view/552 |
| work_keys_str_mv | AT igederusdymahayanaputra identifikasicitraukiranornamentradisionalbalidenganmetodemultilayerperceptron AT madewinduantarakesiman identifikasicitraukiranornamentradisionalbalidenganmetodemultilayerperceptron AT gedeaditrapradnyana identifikasicitraukiranornamentradisionalbalidenganmetodemultilayerperceptron AT imadedendimaysanjaya identifikasicitraukiranornamentradisionalbalidenganmetodemultilayerperceptron |