To cultivate these things it is necessary to be divided into three sizes: Small, medium and large.īatik in Indonesia has various types of patterns, which are arranged repeatedly to illustrate the basic motifs of cloth as a whole. But as we know that batik motifs have a different style. The result of the process will be the index value of each type of batik. The next step is done by using wavelet transform and invariant moment. Once the motifs of batik can be identified, then from the motif can be used 4 functions, texture is the average, energy, entropy and standard deviation. Then proceed with feature extraction process by taking the characteristic of batik by using edge detection method. To do this research, first do a pre-processing process where the batik image changes first into grayscale. His trademark is in the form of his motive. As we know that batik has a distinctive characteristic of the origin of the region. Abstract This journal discusses the retrieval system for identification which is included in the application of images based on Indonesian batik motif. Untuk mengatasi hal tersebut maka perlu maka perlu dibagi menjadi tiga ukuran: Kecil, sedang dan besar. Namun seperti yang kita tahu bahwa motif batik ada yang memiliki kesamaan namun memiliki ukuran yang berbeda. Hasil dari proses tersebut akan menjadi nilai indeks setiap jenis batik. Langkah selanjutnya setelah diperoleh 4 fungsi karakteristik maka perlu lebih di spesifisikan menggunakan metode wavelet transform dan invariant moment. Setelah motif batik dapat dikenali, maka dari motif tersebut dapat dicari 4 fungsi karakteristik tekstur adalah mean, energi, entropi dan standar deviasi. Kemudian dilanjutkan dengan proses ekstraksi fitur dengan cara mengambil karakteristik motif batik dengan menggunakan metode deteksi tepi.
Untuk melakukan penelitian ini, pertama-tama dilakukan proses pre-processing dimana gambar batik diubah terlebih dahulu menjadi grayscale. Ciri khasnya terdapat pada bentuk motifnya. Seperti yang kita tahu bahwa motif batik memiliki ciri khas yang unik sesuai asal daerahnya.
The Advantage threshold algorithm is the simplicity of similarity image process when the performance of CBIR becomes more reliable.Ībstrak Jurnal ini membahas tentang sistem retrieval untuk identifikasi yang termasuk dalam pemrosesan gambar berdasarkan motif batik Indonesia. After that proceed by comparing the value of grade representation of the image by using the min operator in fuzzy logic. High grade values indicates that the characteristic image of the sample (query) is similar to the image database and others. It will display the image based on the representation of the highest grade in each query image, which has been compared with the image database. Basically Grade value calculation is using the fuzzyfication process to get the similarity values with the S-curve which is then used as input to perform retrieval of image with the threshold algorithm. In calculating the percentage of similarity is not only based on performance measurement precision but also the image of the relevant. Although They are different size, direction of slope, and the layout of texture and color. The method is not only recognize the images that have been stored in database, but also be able to find some resemblance ornament image or texture as well as form.
This paper carried out to develop the concept of CBIR by using Wavelet Transform Methods for feature form and texture extraction and adaptive histogram for feature color extraction.
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