Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders
Personal disorder is a type of mental illness. People with personal disorder can not respond changes and demands of life in normal ways. Women with type B personal disorder tend to have high risk of violence. It is important to make early detetction of this personal disorder, so that it can be antic...
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| Үндсэн зохиолчид: | , , |
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| Формат: | UMS Journal (OJS) |
| Хэл сонгох: | eng |
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Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia
2019
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://journals.ums.ac.id/index.php/khif/article/view/7923 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1805342489907822592 |
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| author | Hayat, Cynthia Limong, Samuel Sagala, Noviyanti |
| author_facet | Hayat, Cynthia Limong, Samuel Sagala, Noviyanti |
| author_sort | Hayat, Cynthia |
| collection | OJS |
| description | Personal disorder is a type of mental illness. People with personal disorder can not respond changes and demands of life in normal ways. Women with type B personal disorder tend to have high risk of violence. It is important to make early detetction of this personal disorder, so that it can be anticipated properly. This paper reports an architecture model of back propagation neural network (BPPN) for early detection of type B personal disorder. The back propagation process divided into two phases, i.e training and testing. The training process used 43 data and the testing process used 34 data. The output classified into 4 diagnosis category of type B personal disorder, I.e. anti social, borderline, histrionic, and narcissistics. The optimal parameters of BPPN model consist of maximum epoch of 1000, maximum mu of 10000000000, increase mu of 25, decrease mu of 0.1, and neuron hidden layer of 25. The MSE of training is 3.07E-14 and MSE of testing is 1.00E-03. The accuracy of training is 90.7%, while the accuracy of testing is 97.2%. |
| format | UMS Journal (OJS) |
| id | oai:ojs2.journals.ums.ac.id:article-7923 |
| institution | Universitas Muhammadiyah Surakarta |
| language | eng |
| publishDate | 2019 |
| publisher | Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia |
| record_format | ojs |
| spelling | oai:ojs2.journals.ums.ac.id:article-7923 Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders Hayat, Cynthia Limong, Samuel Sagala, Noviyanti backpropagation; early detection; neural network; personality disorders Personal disorder is a type of mental illness. People with personal disorder can not respond changes and demands of life in normal ways. Women with type B personal disorder tend to have high risk of violence. It is important to make early detetction of this personal disorder, so that it can be anticipated properly. This paper reports an architecture model of back propagation neural network (BPPN) for early detection of type B personal disorder. The back propagation process divided into two phases, i.e training and testing. The training process used 43 data and the testing process used 34 data. The output classified into 4 diagnosis category of type B personal disorder, I.e. anti social, borderline, histrionic, and narcissistics. The optimal parameters of BPPN model consist of maximum epoch of 1000, maximum mu of 10000000000, increase mu of 25, decrease mu of 0.1, and neuron hidden layer of 25. The MSE of training is 3.07E-14 and MSE of testing is 1.00E-03. The accuracy of training is 90.7%, while the accuracy of testing is 97.2%. Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia 2019-12-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://journals.ums.ac.id/index.php/khif/article/view/7923 10.23917/khif.v5i2.7923 Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika; Vol. 5 No. 2 December 2019; 115-123 Khazanah Informatika; Vol. 5 No. 2 December 2019; 115-123 2477-698X 2621-038X eng https://journals.ums.ac.id/index.php/khif/article/view/7923/5216 https://journals.ums.ac.id/index.php/khif/article/downloadSuppFile/7923/937 Copyright (c) 2019 Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | backpropagation; early detection; neural network; personality disorders Hayat, Cynthia Limong, Samuel Sagala, Noviyanti Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders |
| title | Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders |
| title_full | Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders |
| title_fullStr | Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders |
| title_full_unstemmed | Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders |
| title_short | Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders |
| title_sort | architecture of back propagation neural network model for early detection of tendency to type b personality disorders |
| topic | backpropagation; early detection; neural network; personality disorders |
| topic_facet | backpropagation; early detection; neural network; personality disorders |
| url | https://journals.ums.ac.id/index.php/khif/article/view/7923 |
| work_keys_str_mv | AT hayatcynthia architectureofbackpropagationneuralnetworkmodelforearlydetectionoftendencytotypebpersonalitydisorders AT limongsamuel architectureofbackpropagationneuralnetworkmodelforearlydetectionoftendencytotypebpersonalitydisorders AT sagalanoviyanti architectureofbackpropagationneuralnetworkmodelforearlydetectionoftendencytotypebpersonalitydisorders |