Abstract
Indonesia is a country with cultural diversity. One of the famous cultural heritages in Indonesia is Woven Fabrics. East Nusa Tenggara Province, especially South Central Timor, is an area that also produces weaving. There are 3 types of woven fabric motifs, namely the Buna, Lotis, and Futus motifs which were inherited from their ancestors. Woven cloth is unique because it is made through a ritual process and is used for traditional ceremonies, weddings, funerals, and so on. However, along with the development of technology, ordinary people increasingly forget the motifs of woven fabrics and have difficulty distinguishing the motifs. The function of this research is to improve the performance of previous studies in the process of finding the similarity of weaving image motifs using discrete wavelet transforms and GLCM. The results are known, calculations using a confusion matrix on discrete wavelet transformation feature extraction and GLCM, comparisons on discrete wavelet transformations produce an accuracy rate of 70% Minkowski matrix, 60% Manhattan matrix, 60% Canberra matrix, 20% Euclidean matrix. Comparison of feature extraction calculations on GLCM produces an average quality of the Minkowski matrix of 90% and the lowest level of accuracy on the Euclidean, Manhattan, and Canberra matrices of 80%.
Highlights
Indonesia is a country with cultural diversity
Berdasarkan hasil perbandingan yang telah diketahui, perhitungan menggunakan confusion matrix pada fitur ekstraksi transformasi wavelet diskrit dan Gray Level Co-Occurance Matrix (GLCM), maka perbandingan pada transformasi wavelet diskrit menghasilkan tingkat accuracy 70% matrix minkowski, 60% matrix manhattan, 60% matrix canberra, 20% matrix euclidean
Level Co-Occurance Matrix (GLCM) pada motif Futus canberra 60%). sedangkan pada Gray Level Comenggunakan sudut 00, 450, 900, dan 1350 dari beberapa Occurance Matrix (GLCM) nilai recall 1, precision 0.83 motif kain tenun (m6 & m1, m7 & m2, m8 & m3, m7 & dan accuracy 90% untuk matriks jarak euclidean, m4, m7 & m5) dalam matriks jarak euclidean, manhattan, canberra dan minkowski
Summary
Gambar 1 Jenis Motif Kain Tenun (GLCM) menggunakan pengukuran metrik jarak minkowski. Proses Cropping wavelet diskrit yaitu haar, daubechies, symlet dan Proses cropping dilakukan guna mangambil citra yang coiflets dapat dilihat pada Gambar 5. Ekstraksi Fitur dengan Gray Level Co-occurance dibutuhkan dapat dilihat pada Gambar 2. Dapat dilihat pada Gambar 4 setelah citra tnun melalui proses grayscalle, dilanjutkan ke tahap ekstraksi fitur tekstur. Rumus 2 citra yang homogen akan memiliki sedikit gray level, sehingga memberikan sedikit Gray Level Co-Occurance Matrix (GLCM) dengan nilai matriks P (i, j) yang relatif tinggi, serta akan menghasilkan sum of squares yang tinggi. Homogen akan memiliki sedikit gray level, sehingga memberikan sedikit Gray Level Co-Occurance Matrix (GLCM) dengan nilai matriks P (i, j) yang relatif tinggi, serta akan menghasilkan sum of squares yang tinggi. Dalam mengukur suatu kemiripan fitur citra terdapat salah satu metode yang umum digunakan yakni matriks jarak. Dapat dilihat bahwa dalam perhitungan nilai error dengan matriks euclidean, manhattan, Jenis Motif Buna
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