PERANCANGAN DAN IMPLEMENTASI JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK MENDIAGNOSA PENYAKIT KULIT

Received: 17 Apr 2015; Published: 31 Oct 2015.
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Section: Research Article
Language: EN
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Abstract

Skin has a great risk to be suffering from disease. Skin disease is easy to see by the other people, which could urge patient to look for health services and medications immediately. However, most people are less conscious about their skin diseases because many new skin disease which not familiar for patient, so that the skin disease can’t be handle and become worse. Information technology could solve those problems by capturing data and deliver optimal output using particular processes. This research aims to overcome the problem by developing a web based information system that implements backpropagation neural network. The symptoms of skin diseases are used as inputs and the match skin disease as output. The architecture of backpropagation neural network in this researchhas four input neurons on input layer, a hidden layer with adjustable amount of neurons and an output neuron on output layer. As a result the most optimal recognition value with validity percentage of 100% on data training and 40% on data testing with training time in 6 hours and 10 minutes using 100000 maximum epoch, 0.0001 minimum error, 0.4 learning rate and 20 neurons in hidden layer.

Keywords
disease, neural network, backpropagation, web based

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