English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

K-Nearest Neighbor is a non-parametric classification algorithm that does not use training data and initial assumptions or models in the calculation process. The quality of the k-Nearest Neighbor classification results is very dependent on distance between object and value of k specified, so the selection for distance measurement method determines the results of classification. This study compares several distance measurement method, including Euclidean distance, Manhattan distance, Tchebychev distance and Cosine distance to see which distance measurement method can work optimally on the k-Nearest Neighbor algorithm. The selection of k values also determines the results of k-Nearest Neighbor classification algorithm, so determining the k value also needs to be considered. The data used in this study is a dataset of cervical cancer. The highest accuracy results obtained using the Cosine distance measurement method that is equal to 92.559% with a value of k = 9. Based on the accuracy values that have been compared, the most optimal distance measurement method is Cosine distance with the best k value obtained is k = 9 even though this distance measurement method has the highest computing time which is equal to 0.898 seconds.

Comparison of Distance Measurement Methods on K-Nearest Neighbor Algorithm For Classification