KLASIFIKASI JENIS TUMOR OTAK BERDASARKAN CITRA GLIOMA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE
DOI:
https://doi.org/10.46880/mtk.v9i2.1887Keywords:
Brain Tumor, Visual Image, SVM, GLCMAbstract
This study uses the Support Vector Machine method, where this method aims to obtain a classification model that has high accuracy or small error in classifying an image. The development of the medical world today is closely related and cannot be separated from the development of information technology that continues to grow. To be able to distinguish Magnetic Resonance Image (MRI) images detected by brain tumors, it is necessary to carry out a classification process using the Support Vector Machine (SVM) method. So we need an application for classifying brain tumors to facilitate medical work in determining the type of brain tumor disease. Image processing will use 2 types of brain tumors, namely Glioma and Meningioma
References
Akbar, F., Rais, A. N., Sobari, I. A., Zuama, R. A., & Rudiarto, B. (2019, Agustus). Klasifikasi Massa Pada Citra Mammogram Berdasarkan Grey Level Co-occurrence (GLCM). IJCCS, Vol. 8, No.1, 59-68
Dzulfikar, M. A. (2014, Oktober). Analisis Distribusi Intensitas RGB Citra Digital untuk Klasifikasi Kualitas Biji Jagung menggunakan Jaringan Syaraf Tiruan. JURNAL FISIKA DAN APLIKASINYA, VOLUME 10, NOMOR 3.
Rizal, R. A., Gulo, S., & Sihombing, O. D. (2019, Agustus). Analisis Gray Level Co-occurence Matrix (GLCM) Dalam Mengenali Citra Expresi Wajah. Jurnal Mantik, Vol.3, No.2, 31-38.
Pratama, Wahyu, Ifo . (2018). Analisis Performa Algoritma Naive Bayes pada Deteksi Otomatis Citra MRI. Jurnal Ilmu Pengetahuan dan Teknologi Komputer, Vol.5, No.1, 37-42
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Adam Jordie Sinulingga, Darwis Robinson Manalu, Samuel Manurung
This work is licensed under a Creative Commons Attribution 4.0 International License.
