Klasifikasi Motif Batik Dengan Metode K-Nearest Neighbor Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrix

FREDO, ALFRED (2019) Klasifikasi Motif Batik Dengan Metode K-Nearest Neighbor Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrix. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Batik is a pictorial cloth that has motifs according to the specifics of each region that depict Indonesian culture. The number of batik with a variety of colors and colors is certainly difficult to be classified according to certain classes. This study will classify batik motifs based on their motives using the K-Nearest Neighbor method and extract the features of the Gray Level Co-Occurrence Matrix. The research data are 25 images divided into 5 motifs for each image. The test is carried out by extracting the characteristics of the four angular patterns using the Gray Level Co-Occurrence Matrix, and the resulting angle average for the extraction of the image characteristics itself. Feature extraction data between images are classified based on the K-Nearest Neighbor method. Key Words : Classification, Batik, K-Nearest Neighbor, Gray Level Co-Occurrence Matrix Batik merupakan kain bergambar yang memiliki motif sesuai khas daerah masingmasing yang menggambarkan budaya Indonesia. Banyaknya batik dengan beraneka ragam corak dan warna ini tentunya sulit untuk diklasifikasikan berdasarkan kelas tertentu. Penelitian ini akan melakukan klasifikasi motif batik berdasarkan motif-nya menggunakan metode K-Nearest Neighbor dan ekstrasi ciri Gray Level Co-Occurrence Matrix. Data penelitian sebanyak 25 citra terbagi kedalam 5 motif untuk tiap citranya. Pengujian dilakukan dengan ekstrasi ciri terhadap empat pola sudut menggunakan Gray Level Co-Occurrence Matrix, dan dihasilkan rata-rata sudut terhadap ekstrasi ciri citra itu sendiri. Data ekstraksi ciri antar citra diklasifikasikan berdasarkan metode K-Nearest Neighbor. Kata kunci: Klasifikasi, Batik, K-Nearest Neighbor, Gray Level Co-Occurrence Matrik

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