Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection
The preservation of traditional batik patterns, often transmitted orally and through direct practice across generations, faces significant challenges in the modern era. Globalization introduces the risk of cultural homogenization, potentially diminishing the uniqueness and diversity of these pattern...
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LPPM ISB Atma Luhur
2025-01-01
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| Series: | Jurnal Sisfokom |
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| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2352 |
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| author | wafiq azizah soffiana agustin |
| author_facet | wafiq azizah soffiana agustin |
| author_sort | wafiq azizah |
| collection | DOAJ |
| description | The preservation of traditional batik patterns, often transmitted orally and through direct practice across generations, faces significant challenges in the modern era. Globalization introduces the risk of cultural homogenization, potentially diminishing the uniqueness and diversity of these patterns. Furthermore, the manual recognition of batik motifs is labor-intensive, time-consuming, and requires specialized expertise, rendering it unsuitable for large-scale preservation initiatives. Consequently, the development of technology-based solutions capable of documenting, analyzing, and recognizing batik patterns with efficiency and precision is imperative for safeguarding this cultural heritage. This study aims to address these challenges by developing an automated system for recognizing batik patterns, focusing on Javanese batik motifs—Kawung, Megamendung, and Parang—which serve as foundational designs for the evolution of batik in other regions. The proposed methodology integrates two feature extraction techniques, Histogram of Oriented Gradients (HOG) and Texture Moments, with the Random Forest machine learning algorithm. The research process encompasses four key stages: pre-processing, feature extraction, classification, and system evaluation, where the accuracy of individual and combined feature extraction methods is analyzed. Experimental results reveal that the HOG method achieves an accuracy of 78.99%, while the Texture Moments method yields 81.88%. Notably, the combination of these two methods enhances system performance, achieving the highest accuracy of 86.23%, representing a 4.65% improvement over the single methods. These findings underscore the efficacy of integrating HOG and Texture Moments with the Random Forest algorithm for automated batik pattern recognition. |
| format | Article |
| id | doaj-art-6a56c53019aa4bf4a3944377f2960f2b |
| institution | Kabale University |
| issn | 2301-7988 2581-0588 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj-art-6a56c53019aa4bf4a3944377f2960f2b2025-02-12T07:27:38ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882025-01-0114181410.32736/sisfokom.v14i1.23522015Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detectionwafiq azizah0soffiana agustin1Department of Informatics Engineering, Faculty of Engineering, Universitas Muhammadiyah GresikDepartment of Informatics Engineering, Faculty of Engineering, Universitas Muhammadiyah GresikThe preservation of traditional batik patterns, often transmitted orally and through direct practice across generations, faces significant challenges in the modern era. Globalization introduces the risk of cultural homogenization, potentially diminishing the uniqueness and diversity of these patterns. Furthermore, the manual recognition of batik motifs is labor-intensive, time-consuming, and requires specialized expertise, rendering it unsuitable for large-scale preservation initiatives. Consequently, the development of technology-based solutions capable of documenting, analyzing, and recognizing batik patterns with efficiency and precision is imperative for safeguarding this cultural heritage. This study aims to address these challenges by developing an automated system for recognizing batik patterns, focusing on Javanese batik motifs—Kawung, Megamendung, and Parang—which serve as foundational designs for the evolution of batik in other regions. The proposed methodology integrates two feature extraction techniques, Histogram of Oriented Gradients (HOG) and Texture Moments, with the Random Forest machine learning algorithm. The research process encompasses four key stages: pre-processing, feature extraction, classification, and system evaluation, where the accuracy of individual and combined feature extraction methods is analyzed. Experimental results reveal that the HOG method achieves an accuracy of 78.99%, while the Texture Moments method yields 81.88%. Notably, the combination of these two methods enhances system performance, achieving the highest accuracy of 86.23%, representing a 4.65% improvement over the single methods. These findings underscore the efficacy of integrating HOG and Texture Moments with the Random Forest algorithm for automated batik pattern recognition.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2352classificationbatikhistogram of oriented gradientstexture moments random forest |
| spellingShingle | wafiq azizah soffiana agustin Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection Jurnal Sisfokom classification batik histogram of oriented gradients texture moments random forest |
| title | Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection |
| title_full | Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection |
| title_fullStr | Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection |
| title_full_unstemmed | Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection |
| title_short | Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection |
| title_sort | feature extraction using histogram of oriented gradients and moments with random forest classification for batik pattern detection |
| topic | classification batik histogram of oriented gradients texture moments random forest |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2352 |
| work_keys_str_mv | AT wafiqazizah featureextractionusinghistogramoforientedgradientsandmomentswithrandomforestclassificationforbatikpatterndetection AT soffianaagustin featureextractionusinghistogramoforientedgradientsandmomentswithrandomforestclassificationforbatikpatterndetection |