TRANSFORMASI HOUGH LINEAR UNTUK ANALISIS DAN PENGENALAN BATIK MOTIF PARANG
Abstract
Batik is a craft that has high artistic value. Batik also has become part of Indonesian culture (especially Java) since long. There are so many designs old patterns of Batik. One of traditional batik motif is Parang. Batik Parang have decorative striped pattern and lined tilted. This research analizes characteristic of batik Parang and develops a software to automatically identify motifs of batik Parang image using line feature extraction. The pattern can be identified through a group of points that form the edge of a line and then detected as a line using Hough transform. The research material was 50 image of the sample data and 30 image of research datarespectivelyParang and non Parang. Both the accuracy of Batik Parang and non Parang recognition are 90%.
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Angesti, A. (2015). Klasifikasi Motif Batik Menggunakan Moment Invariants. Skripsi, Universitas Kristen Duta Wacana, Teknik Informatika, Yogyakarta.
Ballard, D. H. (1981). Generalizing The Hough Transform To Detect Arbitary Shape. Pattern Recognition , 13 (2), 111-122.
Canny, J. (1986). A Computatioal Approach to Edge Detection. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 8 (6), 679-698.
Dinas Perindustrian Perdagangan dan Koperasi DIY. (2007). Buku Motif Batik Yogya Ceplok (1 ed.). Yogyakarta, Indonesia: Pena Persada Desktop Publishing.
Dinas Perindustrian Perdagangan dan Koperasi DIY. (2007). Buku Motif Batik Yogya Nitik (1 ed.). Yogyakarta, Indonesia: Pena Persada Desktop Publishing.
Dinas Perindustrian Perdagangan dan Koperasi DIY. (2007). Buku Motif Batik Yogya Parang dan Lereng (1 ed.). Yogyakarta, Indonesia: Pena Persada Desktop Publishing.
Dinas Perindustrian Perdagangan dan Koperasi DIY. (2007). Buku Motif Batik Yogya Semen (1 ed.). Yogyakarta, Indonesia: Pena Persada Desktop Publishing.
Douugherty, G. (2009). Digital Image Processing for Medical Application. New York: Cambridge University Press.
Felixiana, S. (2015). Klasifikasi Batik Menggunakan K-Nearest Neighbour Berbasis Nilai Eccentricity dan Compactness. Skripsi, Universitas Kristen Duta Wacana, Teknik Informatika, Yogyakarta.
Hartono, E. S., Haryono, N. A., & Hapsari, W. (2015). Klasifikasi Motif Batik Berbasis Representasi Bentuk dengan Metode Wavelet Daubechies. Skripsi, Universitas Kristen Duta Wacana, Teknik Informatika, Yogyakarta.
Kusrianto, A. (2013). Batik Filosofi, Motif dan Kegunaan. Yogyakarta: Andi.
Mahmood, N. H., & Mansur, M. A. (2012). Red Blood Cells Estimation using Hough Transform Technique : An International Journal (SIPIJ). Signal and Image Processing , 3 (2), 53-64.
Rangkuti, A. H. (2014). Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network. ComTech , 361-372.
Russ, J. C. (2011). The Image Processing Handbook. India: CRC Press Taylor & Francis Group.
Yodha, J. W., & Kurniawan, A. W. (2014). Pengenalan Motif Batik Menggunakan Deteksi Tepi Canny dan K-Nearest Neighbor. Techno.COM , 251-262.
Yu-Thai, C. (2001). Detecting line segments in an image - a new implementation for Hough Transform. Pattern Recognition Letters , 22 (3-4), 421-429.
DOI: http://dx.doi.org/10.21460/inf.2015.112.412
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