Penerapan K-Nearest Neighbors (KNN) dalam Memprediksi dan Menghitung Akurasi Data Penyakit Stroke
DOI:
https://doi.org/10.55606/juprit.v2i4.3040Keywords:
Classification, Machine Learning, K-Nearest Neighbors (K-NN) Algorithm, Stroke PredictionAbstract
Stroke is a disease characterized by a disruption in brain function caused by a lack of oxygen and blood flow to the brain, affecting various brain functions and causing difficulties in performing activities. The classification of stroke patients is still based on medical records that are not integrated, leading to a longer time for detection. The K-Nearest Neighbors (K-NN) algorithm is a part of machine learning that can be utilized to classify cases, including the classification of stroke patients. K-NN serves as the algorithm to determine classes and incorporate new data inputted in the specified format. In this study, the researcher aims to demonstrate that the classification algorithm of K-Nearest Neighbor with Bagging optimization can be used to determine if someone is affected by stroke. The predictions from this algorithm can facilitate decision-making in the healthcare field quickly.
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