Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor
Papua Island has natural and cultural richness wich is reflected in its batik motifs, such as the Cenderawasih and Tifa motifs. Although batik recognition technology has developed, systems capable of automatically identifying Papua batik motifs are still limited. This research aims to develop a tex...
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| Format: | Article |
| Language: | Indonesian |
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Islamic University of Indragiri
2025-03-01
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| Series: | Sistemasi: Jurnal Sistem Informasi |
| Subjects: | |
| Online Access: | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5008 |
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| author | Dian Dwi Ariani Sitti Zuhriyah Eva Yulia Puspaningrum Mahabintang Pallawabonang |
| author_facet | Dian Dwi Ariani Sitti Zuhriyah Eva Yulia Puspaningrum Mahabintang Pallawabonang |
| author_sort | Dian Dwi Ariani |
| collection | DOAJ |
| description | Papua Island has natural and cultural richness wich is reflected in its batik motifs, such as the Cenderawasih and Tifa motifs. Although batik recognition technology has developed, systems capable of automatically identifying Papua batik motifs are still limited. This research aims to develop a texture recognition system using the Local Binary Pattern (LBP) feature extraction method and K-Nearest Neighbor (KNN) classification. The Cenderawasih motif dataset consists of 115 images, and the Tifa motif dataset consists of 120 images with an 80:20 composition for training and testing data. We tested the KNN model with various k values and found that k = 7 yielded the best results, with accuracy of 97.16%, precision of 97.10%, and F1-score of 97.10%. The developed GUI interface facilitates users in identifying batik motifs, providing prediction results, and texture visualization. The results of this study show that image processing technology could help protect Papuan batik. Future research could improve model accuracy by utilizing larger data sets and classification algorithms to make the models more accurate. |
| format | Article |
| id | doaj-art-423b1652bb1248118a93e06e714ef64f |
| institution | OA Journals |
| issn | 2302-8149 2540-9719 |
| language | Indonesian |
| publishDate | 2025-03-01 |
| publisher | Islamic University of Indragiri |
| record_format | Article |
| series | Sistemasi: Jurnal Sistem Informasi |
| spelling | doaj-art-423b1652bb1248118a93e06e714ef64f2025-08-20T01:55:11ZindIslamic University of IndragiriSistemasi: Jurnal Sistem Informasi2302-81492540-97192025-03-0114262363310.32520/stmsi.v14i2.50081028Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest NeighborDian Dwi Ariani0Sitti Zuhriyah1Eva Yulia Puspaningrum2Mahabintang Pallawabonang3Universitas Handayani MakassarUniversitas Handayani MakassarUniversitas Pembangunan Nasional "Veteran" Jawa TimurUniversitas Handayani MakassarPapua Island has natural and cultural richness wich is reflected in its batik motifs, such as the Cenderawasih and Tifa motifs. Although batik recognition technology has developed, systems capable of automatically identifying Papua batik motifs are still limited. This research aims to develop a texture recognition system using the Local Binary Pattern (LBP) feature extraction method and K-Nearest Neighbor (KNN) classification. The Cenderawasih motif dataset consists of 115 images, and the Tifa motif dataset consists of 120 images with an 80:20 composition for training and testing data. We tested the KNN model with various k values and found that k = 7 yielded the best results, with accuracy of 97.16%, precision of 97.10%, and F1-score of 97.10%. The developed GUI interface facilitates users in identifying batik motifs, providing prediction results, and texture visualization. The results of this study show that image processing technology could help protect Papuan batik. Future research could improve model accuracy by utilizing larger data sets and classification algorithms to make the models more accurate.https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5008papuan batik motifs, local binary pattern, k-nearest neighbor, image processing, classification. |
| spellingShingle | Dian Dwi Ariani Sitti Zuhriyah Eva Yulia Puspaningrum Mahabintang Pallawabonang Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor Sistemasi: Jurnal Sistem Informasi papuan batik motifs, local binary pattern, k-nearest neighbor, image processing, classification. |
| title | Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor |
| title_full | Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor |
| title_fullStr | Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor |
| title_full_unstemmed | Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor |
| title_short | Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor |
| title_sort | identification of papua cenderawasih batik motifs using local binary pattern and k nearest neighbor |
| topic | papuan batik motifs, local binary pattern, k-nearest neighbor, image processing, classification. |
| url | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5008 |
| work_keys_str_mv | AT diandwiariani identificationofpapuacenderawasihbatikmotifsusinglocalbinarypatternandknearestneighbor AT sittizuhriyah identificationofpapuacenderawasihbatikmotifsusinglocalbinarypatternandknearestneighbor AT evayuliapuspaningrum identificationofpapuacenderawasihbatikmotifsusinglocalbinarypatternandknearestneighbor AT mahabintangpallawabonang identificationofpapuacenderawasihbatikmotifsusinglocalbinarypatternandknearestneighbor |