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 high dimension of feature data is one of the problem for computation to classify data, it causing the need for feature extraction to reduce its dimension by retrieving only important information form the features.This research using K-Means Algorithm to extract features and finding hidden pattern form each class then reconstructed by fuzzy membership function and get new pattern. New pattern that obtained is used as abstract features and divided into training data and testing data. Training is done by Support Vector Machine (SVM) algorithm to obtain classification model. SVM classification model is tested using testing data to get classification performance as accuracy and computational time. Based on 5-fold cross validation, this method has a good accuracy using Liver Dataset, Breast Cancer Dataset, and Heart Disease Dataset from UCI Machine Learning Repository. This research shows the ability and capability of K-Means for extracting features in dataset. The result of this research shows that using K-Means as feature extractor can reduce computational time.

K-MEANS SEBAGAI EKSTRAKTOR CIRI PADA KLASIFIKASI DATA DENGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)