Hutagaol, LeonHoss (2025) KLASIFIKASI PENYAKIT MONKEYPOX DENGAN MENGGUNAKAN METODE GLCMLBP DAN ALGORITMA SVM. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
Monkeypox is an infectious disease that requires early detection for effective treatment. This study aims to develop a Monkeypox image classification model with a hybrid approach that combines texture feature extraction using Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP), and classification using the Support Vector Machine (SVM) algorithm. The dataset used consists of 3200 Monkeypox images that have gone through a preprocessing stage including grayscale conversion and median filtering to remove noise. Feature extraction is carried out by combining GLCM (energy, contrast, correlation, homogeneity) and LBP to obtain a more comprehensive texture representation. Classification is carried out by testing various SVM kernels (linear, polynomial, RBF, sigmoid) and manual parameter tuning. Performance evaluation using accuracy, precision, recall, and F1-score metrics shows that the model with combined GLCM-LBP features and RBF kernels achieves the highest accuracy of 94%, with stability and efficient computing time. These results indicate that the hybrid approach of GLCM-LBP and SVM with RBF kernel has great potential in supporting the automatic diagnosis of Monkeypox disease through medical image analysis.
Item Type: | Thesis (Undergraduate) | ||||||||||||
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Contributors: |
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Subjects: | Q Science > QA Mathematics > QA76.6 Computer Programming Q Science > QA Mathematics > QA76.625 Internet Programming R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
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Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
Depositing User: | Mr LeonHoss Hutagaol | ||||||||||||
Date Deposited: | 08 Jul 2025 07:45 | ||||||||||||
Last Modified: | 08 Jul 2025 07:45 | ||||||||||||
URI: | https://repository.upnjatim.ac.id/id/eprint/38899 |
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