BibTex Citation Data :
@article{JMASIF8437, author = {Nurul Sukarno and Panji Wirawan and Satriyo Adhy}, title = {PERANCANGAN DAN IMPLEMENTASI JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK MENDIAGNOSA PENYAKIT KULIT}, journal = {Jurnal Masyarakat Informatika}, volume = {5}, number = {10}, year = {2015}, keywords = {disease, neural network, backpropagation, web based}, 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. }, issn = {2777-0648}, pages = {9--18} doi = {10.14710/jmasif.5.10.8437}, url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/8437} }
Refworks Citation Data :
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.
Article Metrics:
Last update:
Last update: 2024-11-20 10:23:18
The authors who submit the manuscript must understand that the article's copyright belongs to the author(s) if accepted for publication. However, the author(s) grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors should also understand that their article (and any additional files, including data sets, and analysis/computation data) will become publicly available once published under that license. See our copyright policy. By submitting the manuscript to Jmasif, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. Jmasif will not be held responsible for anything arising because of the writer's internal dispute. Jmasif will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. Jmasif allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and Jmasif to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.
