KNN MACHINE LEARNING ARCHITECTURE FOR PNEUMONIA CHEST X-RAY CLUSTERING
DOI:
https://doi.org/10.26418/telectrical.v2i1.78604Keywords:
K-Nearest Neighbor, Machine Learning, Chest X-Ray, Classification, Artificial IntelligentAbstract
In the last century, the use of machine learning, especially the K-Nearest Neighbor (KNN) has greatly helped the world of health (medicine). Through action research on image datasets, KNN succeeded and was able to show classification or grouping based on the same characteristics and properties on unlabeled images with higher accuracy and faster than other machine learning methods. This is very useful for the world of health, especially in the use of chest x-rays (chest x-rays) in the medical world. This study aims to optimize the KNN. The results of research to predict the type of covid disease from Chest X Ray image data using the K-Nearest Neighbor architecture are normally not very good with an accuracy value between 1 to 0.965. The result of the research for validation accuracy is constant in 0.95.
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