Analysis of Tuberculosis (TB) on X-ray Image Using SURF Feature Extraction and the K-Nearest Neighbor (KNN) Classification Method

Reyhan Achmad Rizal (Universitas Prima Indonesia)
Nurlela Octavia Purba (Universitas Prima Indonesia)
Lidya Aprilla Siregar (Universitas Prima Indonesia)
Kristina Sinaga (Universitas Prima Indonesia)
Nur Azizah (Universitas Prima Indonesia)

Journal of Applied Information, Communication and Technology (JAICT)
Vol 5, No 2 (2020)

With current technological developments, machine learning has become one of the most popular methods, one of the popular machine learning algorithms is k-nearest neighbors (KNN). Machine learning has been widely used in the medical field to analyze medical datasets, in this study the k-nearest neighbors (KNN) machine learning algorithm will be used because of its good level of accuracy in recognition and is included in the supervised learning algorithm group. The results showed the k-nearest neighbors (KNN) method in recognizing x-ray images of tuberculosis (TB) using SURF feature extraction with an average accuracy of 73%.