Peningkatan Akurasi Klasifikasi Backpropagation Menggunakan Artificial Bee Colony dan K-NN Pada Penyakit Jantung

 (*)Pandito Dewa Putra Mail (Universitas Sriwijaya, Palembang, Indonesia)
 Sukemi Sukemi (Universitas Sriwijaya, Palembang, Indonesia)
 Dian Palupi Rini (Universitas Sriwijaya, Palembang, Indonesia)

(*) Corresponding Author

Submitted: December 5, 2020; Published: January 22, 2021



Heart disease has ranked as the leading cause of death in the world, accounting for around 17.3 million deaths per year with some causes, as high blood pressure, diabetes, cholesterol fluctuation, fatigue, and some others which is collected on dataset. Heart disease dataset that was applied is cleveland heart disease with fourteen (14) data atribute. The method that was applied in this research was using Backpropagation algorithm on heart disease classifying, where will be combined Artificial Bee Colony and k-Nearest Neighbor algorithm for features or atribute choose due to this technique can increase classifier model accuracy which is produced as much as 94,23%.


Heart Disease; Cleveland; Backpropagation; Artificial Bee Colony; K-NN

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