Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM

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 w...

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Autores principales:	Anderias Bai Seran, Aviv Yuniar Rahman, Istiadi Istiadi
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	woven fabric, discrete wavelet, glcm, confusion matrix accuracy. Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/81fe3ec7f87d46e1bd5725c385a8a7a5
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id	oai:doaj.org-article:81fe3ec7f87d46e1bd5725c385a8a7a5
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spelling	oai:doaj.org-article:81fe3ec7f87d46e1bd5725c385a8a7a52021-11-16T13:16:11ZTemu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM2580-076010.29207/resti.v5i5.3484https://doaj.org/article/81fe3ec7f87d46e1bd5725c385a8a7a52021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3484https://doaj.org/toc/2580-0760Indonesia 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%.Anderias Bai SeranAviv Yuniar RahmanIstiadi IstiadiIkatan Ahli Indormatika Indonesiaarticlewoven fabric, discrete wavelet, glcm, confusion matrix accuracy.Systems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 958-966 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	woven fabric, discrete wavelet, glcm, confusion matrix accuracy. Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	woven fabric, discrete wavelet, glcm, confusion matrix accuracy. Systems engineering TA168 Information technology T58.5-58.64 Anderias Bai Seran Aviv Yuniar Rahman Istiadi Istiadi Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM
description	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%.
format	article
author	Anderias Bai Seran Aviv Yuniar Rahman Istiadi Istiadi
author_facet	Anderias Bai Seran Aviv Yuniar Rahman Istiadi Istiadi
author_sort	Anderias Bai Seran
title	Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM
title_short	Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM
title_full	Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM
title_fullStr	Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM
title_full_unstemmed	Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM
title_sort	temu kembali kemiripan motif citra tenun menggunakan transformasi wavelet diskrit dan glcm
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/81fe3ec7f87d46e1bd5725c385a8a7a5
work_keys_str_mv	AT anderiasbaiseran temukembalikemiripanmotifcitratenunmenggunakantransformasiwaveletdiskritdanglcm AT avivyuniarrahman temukembalikemiripanmotifcitratenunmenggunakantransformasiwaveletdiskritdanglcm AT istiadiistiadi temukembalikemiripanmotifcitratenunmenggunakantransformasiwaveletdiskritdanglcm
_version_	1718426489707298816

Temu Kembali Kemiripan Motif Citra Tenun Menggunakan Transformasi Wavelet Diskrit Dan GLCM

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