Implementasi Algoritma K-Means dan Knearest Neighbors (KNN) Untuk Identifikasi Penyakit Tuberkulosis Pada Paru-Paru - Rachmadhany Iman; Basuki Rahmat; Achmad Junaidi (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2024)
In Indonesia, tuberculosis is ranked third in terms of prevalence among countries with the highest tuberculosis burden. Radiological examination, such as X-rays or X-rays, is a method generally used to detect tuberculosis. Chest X-ray examination is one method used to detect tuberculosis. To achieve these goals, the research will combine two powerful data processing techniques. First, the K-Means algorithm will be used to group x-ray image data based on similar characteristics, making it easier to identify typical patterns from images infected with tuberculosis. The research results show the highest accuracy of 93% using data division with a ratio of 80 : 20 with parameter K = 1. These results show that the combined model of the two algorithms can be applied to identify tuberculosis in the lungs.