Optimasi Extreme Learning Machine dengan Particle Swarm Optimization untuk Klasifikasi Penyakit Jantung Koroner

Rasif Nidaan Khofia Ahmadah (Fakultas Ilmu Komputer, Universitas Brawijaya)
Bayu Rahayudi (Fakultas Ilmu Komputer, Universitas Brawijaya)
Yuita Arum Sari (Fakultas Ilmu Komputer, Universitas Brawijaya)

Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 5 No 9 (2021): September 2021

Heart disease is the leading cause of death globally. Several factors that can trigger heart disease include smoking, blood pressure, diabetes, lifestyle, diet, and stress levels. The minimal number of health workers in Indonesia and the different abilities of each doctor in diagnosing patients with heart disease, so that a system is needed to automatically diagnose the disease which functions to assist doctors and overcome delays in inpatient treatment. This system is a classification system using the Particle Swarm Optimization method and the Extreme Learning Machine for the diagnosis of heart disease, where the Particle Swarm Optimization method is used to optimize the parameters of the Extreme Learning Machine. In the tests carried out, the system succeeded in providing an accuracy value of 86%. This also shows that the use of PSO-ELM can increase the accuracy value than using the ELM method only in diagnosing heart disease.