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

Chronic kidney disease is a disease with the largest rate of expenditure in the world.The disease often does not indicate any symptoms that occur as a disease in general. Therefore, this research was conducted with the aim of being able to detect the disease early before being diagnosed to a more serious stage. The application of individual C4.5 algorithm models and PSO-based C4.5 algorithms with bagging techniques are carried out to determine which models provide the best results in detecting chronic kidney disease. The selection of both models is considered because the C4.5 algorithm is one of the best data mining algorithms, but tends to have weaknesses in overlapping data, classes and many attributes. Therefore, Particle Swarm Optimization (PSO) and bagging techniques are also chosen as alternatives in overcoming the weaknesses in the C4.5 algorithm. From the results of the study it was found that the algorithm model C4.5 based PSO with bagging technique is able to select attributes so so that it could improve the value of accuracy better with a result of 99.70% compared to individual models of C4.5 algorithms that produce an accuracy value of 91.72% only.

Optimasi Akurasi Algoritma C4.5 Berbasis Particle Swarm Optimization dengan Teknik Bagging pada Prediksi Penyakit Ginjal Kronis