Using Artificial Neural Network for System Education Eye Disease Recognition Web-Based

Author:

Rismayani 1,Pineng Martina2,Herlinda 1

Affiliation:

1. Dipa Makassar University

2. Kristen Indonesia Toraja University

Abstract

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

Publisher

Trans Tech Publications, Ltd.

Subject

General Medicine

Reference26 articles.

1. Infodatin-Gangguan-penglihatan-2018.pdf., Accessed: Jul. 29, 2021. [Online]. Available: https: //pusdatin.kemkes.go.id/resources/download/pusdatin/infodatin/infodatin-Gangguan-penglihatan-2018.pdf.

2. D. J. Livingstone, Artificial Neural Networks: Methods and Applications. Humana Press, (2011).

3. Jaringan Saraf Tiruan (JST)., https://www.kajianpustaka.com/2016/11/jaringan-saraf-tiruan-jst.html (accessed Jul. 30, 2021).

4. S. N. Rajak and J. Sandford-Smith, Eye Diseases in Hot Climates. JP Medical Ltd, (2015).

5. S. Verma, The Education System: The Fault In Its code. Notion Press, (2018).

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