Abstract
Batik motifs are the base or the blueprint of batik patterns which serve as the core of the batik image design, and therefore the meaning of a sign, symbol or logo in a batik work can be revealed through its motifs. Visual identification requires visual skills and knowledge in classifying patterns formed in a batik image. Lack of media providing information on batik motifs makes the public unable to have sufficient information about batik motifs. Looking at this phenomenon, this study is conducted in order to perform visual identification using a computer that can assist and facilitate in identifying the types of batik. The methods used for batik image recognition are the Co-occurrence Matrix method to provide extraction of batik texture features, and the Geometric Moment Invariant method, while K Nearest Neighbor is used to classify batik images. The results on the accuracy values obtained reveal that the of 80%, compared to the accuracy value result using the Co-occurrence Matrix method that is 70%.
Highlights
the blueprint of batik patterns which serve as the core of the batik image design
symbol or logo in a batik work can be revealed through its motifs
Visual identification requires visual skills and knowledge in classifying patterns formed in a batik image
Summary
Citra batik yang dihasilkan dari proses akuisisi data akan dilakukan proses pemotongan untuk memudahkan proses selanjutnya dengan ukuran 256 x 256 pixel. Citra batik yang belum di crop memiliki ukuran pixel yang besar,sehingga untuk mempermudah proses selanjutnya citra di crop dengan ukuran 256 x 256 pixel seperti pada gambar dibawah ini. Ekstraksi Ciri Ekstraksi ciri dilakukan menggunakan fitur tekstur citra dan bentuk. Moment, entropy, probability, dihitung untuk setiap Co-occurrence Matrix, untuk lebih menghemat waktu komputasi jumlahk an terlebih dahulu empat nilai masingmasing arah 00, 450, 900, dan 1350. Pertama segmentasi background untuk memisahkan objek dengan background dari citra, kemudian threshold sehingga menjadi gambar biner, dengan menggunakan tolak ukur pengubahan nilai pixel apakah menjadi 0 (hitam) atau 225 (putih). Kemudian hitung geometric moment 1 sampai dengan 4 untuk menghitung translasi, skala dan rotasi menggunakan persamaan :. Diagram alir proses ekstraksi ciri bentuk dapat dilihat pada gambar dibawah ini: Gambar 5.
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