Penerapan K-Fold Cross Validation untuk Menganalisis Kinerja Algoritma K-Nearest Neighbor pada Data Kasus Covid-19 di Indonesia - Hafid, Hardianti (Journal of Mathematics, Computations and Statistics, 2023)
The Covid-19 pandemic has been a global challenge in recent years. This virus has impactedmost aspects of human life, including health, the economy, and society. Indonesia is one of the affectedcountries that has now entered an endemic phase. This research aims to apply the K-Fold Cross Validationmethod to analyze the performance of the K-Nearest Neighbor (K-NN) algorithm on Covid-19 cases datain Indonesia, in order to measure how accurately the K-NN model can predict Covid-19 cases. The resultsobtained using 30-Fold cross-validation with a value of k=5 show an accuracy rate of 68.65% and a kappavalue of 0.5123. These results indicate that the K-NN model is capable of providing adequate predictionswith a higher level of agreement. This research provides a deeper understanding of the performance of theK-NN algorithm in the context of Covid-19 cases data in Indonesia, which can be used as a foundation forfurther improvements in modeling and understanding Covid-19 case data.