Batik is an Indonesian cultural heritage that was recognized by UNESCO in 2009. One of the famous batik producing regions in Indonesia is Pekalongan City, Central Java. Pekalongan Batik has a distinctive characteristic compared to other regions, namely from the aspect of color and motif. Pekalongan batik uses bright colors and flora motifs. However, there are some batik craftsmen who still use dark colors. Identify Pekalongan’s typical batik motifs become an obstacle for tourists that are visiting Indonesia. There needs to be an automatic identification system to recognize Pekalongan Batik motifs. The automatic identification system of batik images can contribute to the development of technology in the field of artificial intelligence. This research was conducted by collecting image data taken through observation, interviews and literature review. The image data obtained are four batik motifs. 5 images will be taken from each motif and will be implemented into the system using Matlab R2014a. The next process is feature extraction using the Gray Level Co-Occurrence Matrix (GLCM) method to get information in the batik image. The author then identifies image using the Backpropagation method to obtain the epoch value, learning rate, and accuracy value based on the identification process. The tested system obtained the highest accuracy of 91.2% with epoch 100 and learning rate 0.03 on Sogan batik, 89.6% accuracy with epoch 100 and learning rate 0.02 on Jlamprang batik. 87.2% accuracy was obtained from Cap Kombinasi batik and Tiga Negeri batik with epoch 100, the learning rate was 0.01 and 0.04 respectively. The results of identifying images of Pekalongan batik can be implemented in a more interactive application.
The aim of this work is to present an automated method that assists diagnosis of normal and abnormal MR images. The diagnosis method consists of four stages, pre-processing of MR images, skull Striping, feature extraction, feature reduction and classification. After histogram equalization of image, the features are extracted based on Dual-Tree Complex wavelet transformation (DTCWT). Then the features are reduced using principal component analysis (PCA). In the last stage two classification methods, k-means clustering and Probabilistic neural network (PNN) are employed. Our work is the modification and extension of the previous studies on the diagnosis of brain diseases, while we obtain better classification rate with the less number of features and we also use larger and rather different database.
Batik merupakan warisan budaya Indonesia yang harus kita jaga dan lestarikan. Proses melestarikannya yaitu dengan pendataan identitas batik tersebut secara komputerisasi. Proses tersebut diawali dengan pengenalan pola untuk mencari informasi dari citra batik tersebut menggunakan proses ekstraksi ciri dengan metode GLCM (Gray Level Co-Occurrence Matrix) dan Filter Gabor, kemudian proses klasifikasi menggunakan Jaringan Syaraf Tiruan. Penelitian ini membuat sistem ekstraksi ciri citra batik yang akan digunakan untuk proses selanjutnya yaitu klasifikasi yang dapat digunakan untuk pendataan citra batik, khususnya batik Pekalongan. Pada penelitian ini proses pengumpulan data melalui tiga cara, yaitu observasi, wawancara dan studi pustaka. Dalam pengimplementasiannya menggunakan Matlab 2010a. Pengujian menggunakan empat sampel citra batik tradisional Pekalongan, setiap citra dibagi menjadi beberpa bagian dan selanjutnya diuji dengan metode tersebut. Hasil penelitian ini telah menghasilkan beberapa niai metode GLCM dan hasil citra proses ekstraksi ciri metode Filter Gabor yang dapat digunakan untuk proses klasifikasi citra batik.
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