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Ananda, Jihan Suci
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Physics

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Journal : JOURNAL ONLINE OF PHYSICS

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CLASSIFICATION ANALYSIS OF BRAIN TUMOR DISEASE IN RADIOGRAPHIC IMAGES USING SUPPORT VECTOR MACHINES (SVM) WITH PYTHON Ananda, Jihan Suci; Fendriani, Yoza; Pebralia, Jesi
JOURNAL ONLINE OF PHYSICS Vol. 9 No. 3 (2024): JOP (Journal Online of Physics) Vol 9 No 3
Publisher : Prodi Fisika FST UNJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jop.v9i3.36270

Abstract

This research examines the analysis of brain tumor disease classification using radiographic images using the Python-based Support Vector Machines (SVM) method. Data was collected from the Kaggle platform with four main categories of brain tumors: normal, pituitary, glioma, and meningioma. The data is then processed, including cleaning, pixel intensity normalization, and feature extraction to distinguish brain tumor characteristics. The data were visualized to understand the distribution and characteristics of the tumor. With the implementation of Python, visual analysis becomes efficient. The SVM model was trained and evaluated, showing an accuracy of 90% with good evaluation metrics such as MAE, MSE, RMSE, and F1-SCORE. The results show that SVM has excellent potential as a diagnostic tool to support the identification and treatment of brain tumors.
Co-Authors Pebralia, Jesi Yoza Fendriani