Implementasi Algoritma K-Means dan Knearest Neighbors (KNN) Untuk Identifikasi Penyakit Tuberkulosis Pada Paru-Paru
DOI:
https://doi.org/10.62951/repeater.v2i3.77Keywords:
Lungs, Tuberculosis, X-Ray Image, K-Means, KNNAbstract
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.
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