Optimasi Jaringan Syaraf Tiruan Backpropagation Menggunakan Particle Swarm Optimization untuk Deteksi Penderita Penyakit Jantung

OKTAWANDARI, HARGIANTI HENNI (2014) Optimasi Jaringan Syaraf Tiruan Backpropagation Menggunakan Particle Swarm Optimization untuk Deteksi Penderita Penyakit Jantung. Skripsi, Fakultas Ilmu Komputer.

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Abstract

According to data from the Student Association of Epidemiology School of Public Health, Hasanuddin University (HIMAPID), in Indonesia, with a population of around 237 million people, there were 6.6 million babies born each year and 48,800 of them are already bearing heart disease. To classify whether a person is indicated for heart disease, this current research applied Artificial Neural Network model of Backpropagation and Backpropagation Neural Network models that its weight are optimized using Particle Swarm Optimization with the aim of minimizing the mean squared error (MSE). Both of those methods will be sought through the highest accuracy using Confusion Matrix and the impairment of RMSE for then applied to the Graphical User Interface (GUI). The data that used are from UCI Machine Learning respository with the observation of 270 participants consisting of 13 variables were determined to do the detection. The test results show the accuracy of BPNN + PSO is higher than BPNN, ie, 87.7% and RMSE values decreased from 1.0862 becomes 0.3403.

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