Perbandingan Jaringan Saraf Tiruan LVQ Dengan Backpropagation Dalam Deteksi Dini Penyakit Jantung Koroner - Mohammad Setya Adi Fauzi; Bayu Rahayudi; Candra Dewi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Coronary heart disease is one of the highest causes of death in the world. The World Heart Federation estimates the number of deaths from this disease in Southeast Asia to reach 1.8 million cases in 2014. In Indonesia in 2013 recorded 883,447 people diagnosed with coronary heart disease with the majority of patients aged 55-64 years and the death rate due to this disease is enough high, ie 45% of all deaths in Indonesia, so early detection of coronary heart disease is very important for the risk of this disease can be minimized. One of the popular machine learning techniques and fits in this case is the artificial neural network. Artificial neural networks are systems that are inspired by reasoning processes in human neural networks. In this study the authors compared the performance of artificial neural network LVQ method and Backpropagation method for early detection of coronary heart disease. The variables of coronary heart disease used in this study were gender, age, pulse, systolic blood pressure, cholesterol, blood sugar, triglycerides, chest pain, shortness of breath, and cough. From the results of this study showed that the Backpropagation method is better than the LVQ method with the comparison of the accuracy value of training of 95,99097% for Backpropagation compared to 66,89659% for LVQ and the accuracy value of testing of 68,76034% for Backpropagation compared to 54,30313% for LVQ.