Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Advances in Intelligent Informatics
Ni Made Ika Marini Mandenni
Department of Information Technology, Udayana University, Denpasar, Bali
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparison of SVM kernels in brain tumor image classification using GLCM feature extraction I Gede Susrama Mas Diyasa; Kraugusteeliana Kraugusteeliana; Hanif Nur Fadlilah; Yisti Vita Via; Anita Muliawati; Allan Ruhui Fatmah Sari; Erna Harfiani; Ni Made Ika Marini Mandenni; Deshinta Arrova Dewi
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The human brain plays a vital role in regulating bodily functions, and abnormal cell growth may lead to life-threatening brain tumors. Automated computer-aided diagnosis systems are therefore essential to support early detection from MRI images. This study investigates brain tumor classification using Gray Level Co-occurrence Matrix (GLCM) feature extraction combined with Support Vector Machine (SVM) classification. Unlike prior works that typically employ a single kernel configuration, this study conducts a systematic comparison of four SVM kernels linear, polynomial, radial basis function (RBF), and sigmoid under a consistent preprocessing pipeline and structured hyperparameter tuning framework. GLCM features including energy, contrast, correlation, and homogeneity were extracted at multiple distances and angles. Kernel performance was evaluated using controlled hyperparameter search procedures to ensure fair comparison across models. Experimental results on a binary MRI dataset consisting of 2,800 images demonstrate that the RBF kernel achieved the highest accuracy of 96% with C = 100 and gamma = 10, outperforming polynomial (74%), linear (72%), and sigmoid (71%) kernels. The findings highlight the importance of systematic kernel evaluation and parameter sensitivity analysis in texture-based medical image classification. The proposed GLCM–SVM framework provides a computationally efficient and interpretable approach that may support preliminary decision-aid systems for brain tumor screening.
Co-Authors Allan Ruhui Fatmah Sari Anita Muliawati Deshinta Arrova Dewi Erna Harfiani Hanif Nur Fadlilah I Gede Susrama Mas Diyasa Kraugusteeliana Kraugusteeliana Yisti Vita Via