Perancangan Dan Implementasi Jaringan Saraf Tiruan Backpropagation Untuk Mendiagnosa Penyakit Kulit

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.

Implementasi Metode Forward Chaining untuk Identifikasi Penyakit Kulit dan Alternatif Penanganannya

“…Kulit adalah salah satu bagian organ tubuh yang digunakan sebagai indra peraba dan penunjang kehidupan manusia [1]. Kulit adalah bagian dari panca indera manusia dan organ tubuh yang pertama kali menerima rangsangan dari luar [2].…”

Evaluasi Prediksi Curah Hujan dengan Algoritma Backpropogation di BMKG Cilacap

“…Metode jaringan syaraf tiruan backpropagation adalah algoritma untuk memperkecil tingkat ke-error-an dengan cara menyeimbangkan, menyesuaikan bobotnya berdasarkan perbedaan output dan target yang akan dicapai. [11] 11. Langkah 10: Uji kondisi berakhir (akhir iterasi).…”

Expert System of Diagnosing Building Damage due to Earthquake using Backpropagation Artificial Neural Network Method

“…The BP-ANN approach has been used to detect lung disorders and shows good performance by about 99.75% of accuracy [3]. Additionally, it has also been successfully applied to diagnose children's skin diseases and provide by about 87.22% of accuracy [4].…”

“…The architecture in Fig. 2 is an example of a multilayer neural network architecture consisting of an input layer (x), a hidden layer (z), and an output layer (y) [4].…”