Klasifikasi Tumor Otak Menggunakan Local Binary Pattern dan SVM Classifier

Wahyu Ardiantito S (Universitas Negeri Medan)
Stacyana Jesika Surianto (Universitas Negeri Medan)
Suci Ramadhani (Universitas Negeri Medan)
Willy Pramudia Ananta (Universitas Negeri Medan)

Student Research Journal
Vol. 1 No. 6 (2023): Desember : Student Research Journal

Brain tumors are abnormal cell growths in brain tissue that can be life-threatening. This study aims to classify brain tumors to help early diagnosis. The method used is to extract features from brain MRI images using Local Binary Pattern (LBP) and then classified with Support Vector Machine (SVM). The data used were 2044 brain MRI images consisting of 3 classes namely meningioma, no tumor, and pituitary. The best results were obtained using LBP with a radius of 1 and the number of neighbors 8, while the best SVM model used the RBF kernel with a C value of 50, resulting in 88% accuracy, 86% precision, and 87% recall. It can be concluded that the combination of LBP and SVM methods is effective enough to classify brain tumor types to support early diagnosis.