Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction
One of the arts in Surakarta culture is batik cloth. A batik is a form of heritage from the nation's ancestors whose manufacturing process must use specific tools and materials. Surakarta's typical batik has many patterns and motifs, such as Sawat, Satriomanah, and Semenrante. The pattern...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | UMS Journal (OJS) |
| Language: | eng |
| Published: |
Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia
2023
|
| Subjects: | |
| Online Access: | https://journals.ums.ac.id/index.php/khif/article/view/21207 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1805342476633899008 |
|---|---|
| author | Riadi, Imam Fadlil, Abdul D.E Purwadi Putra, Izzan Julda |
| author_facet | Riadi, Imam Fadlil, Abdul D.E Purwadi Putra, Izzan Julda |
| author_sort | Riadi, Imam |
| collection | OJS |
| description | One of the arts in Surakarta culture is batik cloth. A batik is a form of heritage from the nation's ancestors whose manufacturing process must use specific tools and materials. Surakarta's typical batik has many patterns and motifs, such as Sawat, Satriomanah, and Semenrante. The pattern is a picture framework whose results will display the type of batik. A batik may resemble one type and another, so a classification technique is needed to determine the type of batik. This study aims to develop a classification method for batik cloth using the Naïve Bayes classification technique. The feature extraction used is the Gray Level Co-Occurrence Matrix (GLCM) to obtain texture values in each image. The stages in this research include pre-processing, feature extraction, classification, and testing. The training data in this study were 200 images for each Sawat, Satriomanah, and Sementrante class obtained from the data augmentation method by flipping, zooming, cropping, shifting, and changing the brightness of the images. The total sample data is 600 images. The amount of training data and data testing was divided three times (60% training and 40% testing), (70% training and 30% testing), and (80% training and 20% testing) for accuracy. In this study, the Naïve Bayes method using WEKA 3.8.6 tools obtained the best accuracy of 97.22% using a 70% percentage split compared to using 80% and 60% percentage splits with a result of 96.66%, this difference occurs due to differences in training data and test data. The results of this study indicate that the Naïve Bayes method can be used to classify batik cloth patterns based on texture feature extraction. |
| format | UMS Journal (OJS) |
| id | oai:ojs2.journals.ums.ac.id:article-21207 |
| institution | Universitas Muhammadiyah Surakarta |
| language | eng |
| publishDate | 2023 |
| publisher | Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia |
| record_format | ojs |
| spelling | oai:ojs2.journals.ums.ac.id:article-21207 Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction Riadi, Imam Fadlil, Abdul D.E Purwadi Putra, Izzan Julda Classification, GLCM, Naïve Bayes, Surakarta Batik Pattern One of the arts in Surakarta culture is batik cloth. A batik is a form of heritage from the nation's ancestors whose manufacturing process must use specific tools and materials. Surakarta's typical batik has many patterns and motifs, such as Sawat, Satriomanah, and Semenrante. The pattern is a picture framework whose results will display the type of batik. A batik may resemble one type and another, so a classification technique is needed to determine the type of batik. This study aims to develop a classification method for batik cloth using the Naïve Bayes classification technique. The feature extraction used is the Gray Level Co-Occurrence Matrix (GLCM) to obtain texture values in each image. The stages in this research include pre-processing, feature extraction, classification, and testing. The training data in this study were 200 images for each Sawat, Satriomanah, and Sementrante class obtained from the data augmentation method by flipping, zooming, cropping, shifting, and changing the brightness of the images. The total sample data is 600 images. The amount of training data and data testing was divided three times (60% training and 40% testing), (70% training and 30% testing), and (80% training and 20% testing) for accuracy. In this study, the Naïve Bayes method using WEKA 3.8.6 tools obtained the best accuracy of 97.22% using a 70% percentage split compared to using 80% and 60% percentage splits with a result of 96.66%, this difference occurs due to differences in training data and test data. The results of this study indicate that the Naïve Bayes method can be used to classify batik cloth patterns based on texture feature extraction. Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia 2023-04-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://journals.ums.ac.id/index.php/khif/article/view/21207 10.23917/khif.v9i1.21207 Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika; Vol. 9 No. 1 April 2023 Khazanah Informatika; Vol. 9 No. 1 April 2023 2477-698X 2621-038X eng https://journals.ums.ac.id/index.php/khif/article/view/21207/8354 https://journals.ums.ac.id/index.php/khif/article/downloadSuppFile/21207/5408 Copyright (c) 2023 Izzan Julda D.E Purwadi Putra, Imam Riadi, Abdul Fadlil https://creativecommons.org/licenses/by/4.0 |
| spellingShingle | Classification, GLCM, Naïve Bayes, Surakarta Batik Pattern Riadi, Imam Fadlil, Abdul D.E Purwadi Putra, Izzan Julda Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction |
| title | Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction |
| title_full | Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction |
| title_fullStr | Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction |
| title_full_unstemmed | Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction |
| title_short | Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction |
| title_sort | batik pattern classification using naive bayes method based on texture feature extraction |
| topic | Classification, GLCM, Naïve Bayes, Surakarta Batik Pattern |
| topic_facet | Classification, GLCM, Naïve Bayes, Surakarta Batik Pattern |
| url | https://journals.ums.ac.id/index.php/khif/article/view/21207 |
| work_keys_str_mv | AT riadiimam batikpatternclassificationusingnaivebayesmethodbasedontexturefeatureextraction AT fadlilabdul batikpatternclassificationusingnaivebayesmethodbasedontexturefeatureextraction AT depurwadiputraizzanjulda batikpatternclassificationusingnaivebayesmethodbasedontexturefeatureextraction |