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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The existence of outliers in the dataset can cause low accuracy in a classification process. Outliers in the dataset can be removed from a preprocessing stage of classification algorithms. Clustering can be used as an outlier detection method. This study applies K-means and a distance matrix to detect outliers and remove them from datasets with class labels. This research used a dataset of students’ academic performance totaling 6847 instances, having 18 attributes and 3 class labels. Preprocessing applies the K-means method to get centroid in each class. The distance matrix is used to evaluate the distance of instance to the centroid. Outliers, which are a different class, will be removed from the dataset. This preprocessing improves the classification accuracy of the kNN algorithm. Data without preprocessing has 72.28 % accuracy, preprocessed data using K-means with Euclidean has 98.42 % accuracy (an increase of 26.14 %), while the K-means with Manhattan has 97.76 % accuracy (an increase of 25.48 %).

Preprocessing kNN algorithm classification using K-means and distance matrix with students’ academic performance dataset