IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON

Authors

  • I Gede Rusdy Mahayana Putra Universitas Pendidikan Ganesha
  • Made Windu Antara Kesiman Universitas Pendidikan Ganesha
  • Gede Aditra Pradnyana Universitas Pendidikan Ganesha
  • I Made Dendi Maysanjaya Universitas Pendidikan Ganesha

Keywords:

Confusion Matrix, Gabor Filter, Identification, Multilayer Perceptron

Abstract

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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References

Kurniawan, B.. “Goresan Anak-anak Sebagai Ide Penciptaan Karya Seni Lukis”. UPT Perpustakaan ISI Yogyakarta, 2014.

Waisnawa, I. M. J., & Yupardhi, T. H.. Pengembangan Ornamen Tradisional Bali (keketusan, pepatran dan kekarangan). Documentation ISI Denpasar, 2014.

Arisandi, B., Suciati, N., & Wijaya, A. Y.. “Pengenalan Motif Batik Menggunakan Rotated Wavelet Filter Dan Neural Network”. JUTI : Jurnal Ilmiah Teknologi. vol. 2. pp 13-19, 2009

I. C. M. Irfan, Sumbodo, “Sistem Klasifikasi Kendaraan Berbasis Pengolahan Citra Digital dengan Metode Multilayer Perceptron,” IJEIS, pp. 139–148, 2017.

Elvinarosa, A.. “Aplikasi Metode Filter Bank Gabor pada Identifikasi Citra Wajah dari Individu yang Bergerak dan Tidak Bergerak”. Institut Teknologi Sepuluh Nopember. 2017

Wanto, Anjar. “Prediksi Produktivitas Jagung Di Indonesia Sebagai Upaya Antisipasi Impor Menggunakan Jaringan Saraf Tiruan Backpropagation”. SINTECH (Science and Information Technology) Journal. vol. 2. pp 53-62, 2019

D. Setiawan, R. Putir, R. Suryanita. “Perbandingan Algoritma Genetika dan Backpropagation pada Aplikasi Prediksi Penyakit Autoimun”. Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika. vol. 5. pp 21-27. 2019

Wirawan, I. M. A. Metode Penalaran Dalam Kecerdasan Buatan. Depok: PT Raja Grafindo Persada. 2017.

Syam, A.M. “Pengenalan Aksara Jawa Tulisan Tangan Dengan Menggunakan Ekstraksi Fitur Zoning Dan Klasifikasi K-Nearest Neigbhour.”S.Kom, Institut Pertanian Bogor.

Febri, T. “Identifikasi Retinoblastoma Menggunakan Extreme Learning Machine.”S.Kom, Universitas Sumatera Utara

Sumantara, I Gusti L. T. et al “Rancang Bangun Aplikasi Pengenalan Ukiran Bali dengan Metode ORB.” Merpati. vol. 5. pp51-56, 2017

Surya, R. et al.”Ektraksi Ciri Metode Gray Level Co-Occurrence Matrix (GLCM) dan Filter Gabor Untuk Klasifikasi Citra Batik Pekalongan.” JPIT. vol. 02. pp23-26, 2017

Arisandi, B. et al.”Pengenalan Motif Batik Menggunakan Rotated Wavelet Filter dan Neural Network.” JUTI. vol. 9. pp13-19, 2009

Wahyudi, E. et al.”Case-Based Reasoning untuk Diagnosis Penyakit Jantung”. IJCCS. vol. 11. pp1-10, 2017

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Published

2021-04-21

How to Cite

[1]
I. G. R. M. . Putra, M. W. A. . Kesiman, G. A. . Pradnyana, and I. M. D. . Maysanjaya, “IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON”, SINTECH Journal, vol. 4, no. 1, pp. 29-39, Apr. 2021.