Irsalinda, Nursyiva and Sugiyarto, Sugiyarto and Ratna, Indah Pattern Recognition Using Multiclass Support Vector Machine Method with Local Binary Pattern as Feature Extraction. STI. ISSN e-ISSN:2580-4391 p-ISSN:2580-4405
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Abstract
Pattern recognition is a scientific discipline usually used to classify objects into a number of categories or classes through a feature
extraction method applied to recognize an object accurately. Meanwhile, Local Binary Pattern (LBP) is a texture analysis method
which uses statistical and structural models for feature extraction. Moreover, a Support Vector Machine (SVM) method is normally
used to solve non-linear problems in high dimensions to obtain an optimal solution by finding the best hyperplane through the
maximizing of the margin between two data classes. Pattern recognition in paintings using machine learning has never been done in
any research. Meanwhile it is very important in the future to be able to serve as a verification system for novelty works of art at the
stage of filing for intellectual property rights. Therefore, this study aimed to apply pattern recognition with LBP feature extraction
method and multiclass SVM classification method to classify the flow of several classes of painting works including expressionism,
fauvism, naturalism, realism, and romanticism. The best evaluation results using this method were obtained in the training and
testing data combination of 90:10 with an accuracy rate of 83%. Therefore, it can be concluded that machine learning in pattern
recognition of painting works can be applied.
| Item Type: | Artikel Umum |
|---|---|
| Subjects: | Q Science > QA Mathematics |
| Divisi / Prodi: | Faculty of Applied Science and Technology (Fakultas Sains Dan Teknologi Terapan) > S1-Mathematics (S1-Matematika) |
| Depositing User: | Dr Sugiyarto Surono |
| Date Deposited: | 29 Aug 2022 02:20 |
| Last Modified: | 29 Aug 2022 02:20 |
| URI: | http://eprints.uad.ac.id/id/eprint/36548 |
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