LOMBOK PEARL QUALITY CLASSIFICATION USING A COMBINATION OF FEATURE EXTRACTION AND ARTIFICIAL NEURAL NETWORKS BASED ON SHAPE

  • Bahtiar Imran (1) Universitas Teknologi Mataram
  • Ahmad Yani (2*) Universitas Teknologi Mataram
  • Rudi Muslim (3) Universitas Teknologi Mataram
  • Zaeniah Zaeniah (4) Universitas Teknologi Mataram

  • (*) Corresponding Author
Keywords: classification, pearl, glcm, artificial neural network

Abstract

Lombok is attracted to the Moto GP event, which is held annually. Various tourism brands are owned by the island of Lombok, one of which is Mutiara. The ideal Pearl is perfectly round and smooth, but there are a variety of other shapes as well. One method that can be used to process Pearl's image is Computer Vision. For that, it is necessary to have a way to classify the quality of a Pearl based on its shape. The purpose of this study is to propose a system for pearl image classification by combining feature extraction with artificial neural networks. The method used in this study is GLCM feature extraction and Neural Networks. The proposed system can provide good classification results by combining the GLCM method and the Neural Network. This study uses Epochs 5, 10, 15, 30, 50, 100, 200, 300, and 500 with a learning rate of 0.5. The results of this study indicate that Epoch 100 gives the highest accuracy, 91.66%.

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References

Agatonovic-Kustrin, S., & Morton, D. W. (2012). The use of UV-Visible reflectance spectroscopy as an objective tool to evaluate pearl quality. Marine Drugs, 10(7), 1459–1475. https://doi.org/10.3390/md10071459

Agatonovic, S. (2015). The Use of Probabilistic Neural Network and UV Reflectance Spectroscopy as an Objective Cultured Pearl Quality Grading Method. Modern Chemistry and Applications, 03(02). https://doi.org/10.4172/2329-6798.1000152

Akbar, A., Siswojo, B., & Suyono, H. (2017). Klasifikasi Mutu Mutiara Berdasarkan Bentuk Dan Ukuran Menggunakan K-Nearest Neighbor. CESS (Journal of Computer Engineering System and Science), 2(2), 93–97.

Alazawi, S. A., Shati, N. M., & Abbas, A. H. (2019). Texture features extraction based on GLCM for face retrieval system. Periodicals of Engineering and Natural Sciences, 7(3), 1459–1467. https://doi.org/10.21533/pen.v7i3.787

Cheng, Q., Hu, W., & Bai, Z. (2021). Research Trends of Development on Pearl Bivalve Mollusks Based on a Bibliometric Network Analysis in the Past 25 Years. Frontiers in Marine Science, 8(April), 1–14. https://doi.org/10.3389/fmars.2021.657263

Lapico, A., Sankupellay, M., Cianciullo, L., Myers, T., Konovalov, D. A., Jerry, D. R., Toole, P., Jones, D. B., & Zenger, K. R. (2019). Using Image Processing to Automatically Measure Pearl Oyster Size for Selective Breeding. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019, 90–97. https://doi.org/10.1109/DICTA47822.2019.8945902

Liu, X., Jin, S., Yang, Z., Królczyk, G., & Li, Z. (2022). Measuring Shape Parameters of Pearls in Batches Using Machine Vision: A Case Study. Micromachines, 13(4), 1–14. https://doi.org/10.3390/mi13040546

Meyer, J. B., Cartier, L. E., Pinto-Figueroa, E. A., Krzemnicki, M. S., Hänni, H. A., & McDonald, B. A. (2013). DNA Fingerprinting of Pearls to Determine Their Origins. PLoS ONE, 8(10). https://doi.org/10.1371/journal.pone.0075606

Ozaki, R., Kikumoto, K., Takagaki, M., Kadowaki, K., & Odawara, K. (2021). Structural colors of pearls. Scientific Reports, 11(1), 1–10. https://doi.org/10.1038/s41598-021-94737-w

Pathak, B., & Barooah, D. (2013). Texture analysis based on the gray-level co-occurence martix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(9), 4206–4212. http://www.ijareeie.com/upload/2013/september/7_-TEXTURE.pdf

Suharjito, Imran, B., & Girsang, A. S. (2017). Family relationship identification by using extract feature of gray level co-zoccurrence matrix (GLCM) based on parents and children fingerprint. International Journal of Electrical and Computer Engineering, 7(5), 2738–2745. https://doi.org/10.11591/ijece.v7i5.pp2738-2745

Surya, R. A., Fadlil, A., & Yudhana, A. (2017). Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix ( GLCM ) dan Filter Gabor untuk Klasifikasi Citra Batik Pekalongan. Jurnal Informatika:Jurnal Pengembangan IT (JPIT , Vol. 02, No. 02, Juli 2017, 02(02), 23–26.

Tian, C. (2009). A Computer Vision-Based Classification Method for Pearl Quality Assessment. International Conference on Computer Technology and Development, 73–76.

Tsai, T.-H., & Zhou, C. (2021). Rapid detection of color-treated pearls and separation of pearl types using fluorescence analysis. Applied Optics, 60(20), 5837. https://doi.org/10.1364/ao.427203

Xuan, Q., Fang, B., Liu, Y., Wang, J., Zhang, J., Zheng, Y., & Bao, G. (2018). Automatic Pearl Classification Machine Based on a Multistream Convolutional Neural Network. IEEE Transactions on Industrial Electronics, 65(8), 6538–6547. https://doi.org/10.1109/TIE.2017.2784394

Zhou, Y., Liu, T., Shi, Y., Chen, Z., Mao, J., & Zhou, W. (2016). Automated Internal Classification of Beadless Chinese ZhuJi Fleshwater Pearls based on Optical Coherence Tomography Images. Scientific Reports, 6(September). https://doi.org/10.1038/srep33819

Published
2022-09-13
How to Cite
Imran, B., Yani, A., Muslim, R., & Zaeniah, Z. (2022). LOMBOK PEARL QUALITY CLASSIFICATION USING A COMBINATION OF FEATURE EXTRACTION AND ARTIFICIAL NEURAL NETWORKS BASED ON SHAPE. Jurnal Pilar Nusa Mandiri, 18(2), 167-172. https://doi.org/10.33480/pilar.v18i2.3507
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