Klasifikasi citra HSV aksara kuno pada prasasti tembaga Kintamani menggunakan GLRCM dan SVM

HSV image classification of ancient script on copper Kintamani inscriptions using GLRCM and SVM

*Christina Purnama Yanti  -  STMIK STIKOM Indonesia, Indonesia
I Gede Andika  -  STMIK STIKOM Indonesia, Indonesia
Received: 15 Apr 2019; Revised: 9 Dec 2019; Accepted: 18 Jan 2020; Published: 30 Apr 2020; Available online: 5 Feb 2020.
DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.94-99 View
training and testing data
Subject training and testing data
Type Data Set
  Download (174KB)    Indexing metadata
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Article Info
Section: Original Research Articles
Language: ID
Statistics: 312 98
Abstract
The problem of inscription physical damage as one of the historical heritages can be overcome using an image processing technique. The purpose of this study is to design a segmentation application for ancient scripts on inscriptions to recognize the character patterns on the inscriptions in digital form. The preprocessing was carried out to convert images from RGB to HSV. The application used the gray level run length matrix (GLRLM) to extract texture features and the support vector machine (SVM) method to classify the results. The inscription image segmentation was carried out through the pattern detection process using the sliding window method. The application obtained 88.32 % of accuracy, 0.87 of precision, and 0.94 of sensitivity.

Note: This article has supplementary file(s).

Keywords: segmentation; inscription; HSV image; GLRLM; SVM

Article Metrics:

  1. I. W. R. Suarmana, I. W. Ardika, and I. N. Darma Putra, “Pengembangan pusat kota Denpasar sebagai heritage tourism,” Jurnal Master Pariwisata, vol. 4, no. 1, pp. 62–77, 2017.
  2. W. Ika and A. M. Wibowo, “Prasasti anjuk ladang di Nganjuk Jawa Timur (sejarah dan potensinya sebagai sumber pembelajaran sejarah),” Jurnal Sejarah dan Pembelajarannya, vol. 7, no. 1, pp. 82–103, 2017. doi: 10.25273/ajsp.v7i01.1062
  3. S. T. Rasmana, “Letter segmentation of the ancient copper inscriptions using texture-based,” Doctoral thesis, Institut Teknologi Sepuluh Nopember, Indonesia, 2017.
  4. I. K. D. Setiawan, S. T. Rasmana, and M. C. Wibowo, “Analisis fitur citra prasasti logam menggunakan metode gray level run length matrix,” Journal of Control and Network Systems, vol. 4, no. 1, pp. 22–30, 2015.
  5. A. Hidayat and R. N. Shofa, “Self organizing maps (SOM) suatu metode untuk pengenalan aksara Jawa,” Jurnal Siliwangi Seri Sains dan Teknologi, vol. 2, no. 1, pp. 64-70, 2016.
  6. T. Arifianto, “Segmentation character of character Java use adaptive threshold,” M. Eng. Thesis, Institut Teknologi Sepuluh Nopember, Indonesia, 2016.
  7. E. P. Purwandari, R. U. Hasibuan, and D. Andreswari, “Identifikasi jenis bambu berdasarkan tekstur daun dengan metode gray level co-occurrence matrix dan gray level run length matrix,” Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 4, pp. 146-151, 2018. doi: 10.14710/jtsiskom.6.4.2018.146-151
  8. M. M. Galloway, “Texture analysis using gray level run lengths,” Computer Graphics and Image Processing, vol. 4, no. 2, pp. 172-179, 1975. doi: 10.1016/S0146-664X(75)80008-6
  9. N. Neneng, K. Adi, and R. Isnanto, “Support vector machine untuk klasifikasi citra jenis daging berdasarkan tekstur menggunakan ekstraksi ciri gray level co-occurrence matrices (GLCM),” Jurnal Sistem Informasi Bisnis, vol. 6, no. 1, pp. 1–10, 2016. doi: 10.21456/vol6iss1pp1-10
  10. D. J. Bora, A. K. Gupta, and F. A. Khan, “Comparing the performance of L*A*B* and HSV color spaces with respect to color image segmentation,” arXiv:1506.01472 [cs.CV], 2015.
  11. R. P. Rakhmawati, “Sistem deteksi jenis bunga menggunakan nilai HSV dari citra mahkota bunga,” Bachelor thesis, Universitas Stikubank, Indonesia, 2013.
  12. O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. I. M. Setiadi, "Tomatoes classification using K-NN based on GLCM and HSV color space," in International Conference on Inovative and Creative Information Technology, Salatiga, Indonesia, Nov. 2017, pp. 1-6. doi: 10.1109/INNOCIT.2017.8319133
  13. A. Wedianto, H. L. Sari, and Y. S. H, “Analisa perbandingan metode filter gaussian, mean dan median terhadap reduksi noise,” Jurnal Media Infotama, vol. 12, no. 1, pp. 21–30, 2016.

No citation recorded.