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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
Fairuz Astari Devianty
Universitas Pancasila
Computer Science & IT Electrical & Electronics Engineering

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Early Detection Application of Bipolar Disorders Using Backpropagation Algorithm Desti Fitriati; Febri Maspiyanti; Fairuz Astari Devianty
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1936

Abstract

Mental health is an important aspect in realizing overall health and important to be considered as physical health. Mental disorders are classified as difficult to diagnose due to the similarity of symptoms that can occur. In addition, information about mental disorders is inadequate so that it can be difficult for experts to provide a diagnosis of the disorders experienced by patients. The difficulty of experts in diagnosing is usually caused by the similarity of symptoms in mental disorders, such as in schizophrenia and bipolar disorder. Based on these problems, this research would like to conduct an early detection study of bipolar disorder by using screening questionnaire data from 300 respondents and serve as a knowledge base to be processed using the backpropagation algorithm. Based on all the results of testing the backpropagation algorithm that has been done to find out the results obtained accuracy and the highest results of training, the highest results obtained with the total test data correct or suitable is 249 and the wrong data is 1 of 250 test data. If it is calculated by a formula, the resulting accuracy rate is 99.6%. And it can be concluded broadly that the greatest influence of the accuracy of the backpropagation algorithm is based on momentum. Because in testing momentum the highest accuracy can be produced compared to the results of other analyzes.
Co-Authors Desti Fitriati Febri Maspiyanti