Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders

Cynthia Hayat, Samuel Limong, Noviyanti Sagala

DOI: https://doi.org/10.23917/khif.v5i2.7923

Abstract

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%.

Keywords

backpropagation; early detection; neural network; personality disorders

Full Text:

PDF

References

M. H. America, “Personality Disorder,” http://www.mentalhealthamerica.net/conditions/personality-disorder, Amerika, 12 September 2017.

A. P. Association, Diagnostic and Statistical manual of mental Disorders, fourt Edition: primary care version (DSM-IV-PC), Washington DC: American Psychiatric Association, 1995.

D. Barros dan A. Serafim, “Association between personality disorder and violent behavior pattern,” Forensic Science International, vol. 179, no. 1, pp. 19-22, 2008.

R.Arola, H.Antila, P.Riipinen, H.Hakko, K.Riala dan L.Kantojarvi, “Borderline personality disorder and violent criminality in women ; A population base follow-up study of adolescent psychiatric inpatients in Northen Finland,” Forensic Science International, vol. 266, pp. 389-395, 2016.

F. W. Chiueng, J. W. Yu, C. L. Po, H. W. Chih, F. P. Shinn dan W. C. Hung, “Disase-Free Survival Aassessment by Artificial Neural Networks for Hepatocellular Carcinoma patients after Radiofrequency Ablation,” Journal of The Formosan Medical Association, vol. 116, no. 10, pp. 765-773, October 2017.

P. Hu, “Identification of Psychological Paterns using Neural Networks Approach,” Digital Commons University of Nebraska Lincoln, Nebraska, 2010.

E. Budianita, S. Sanjaya, F. Syafria dan Redho, “Penerapan Metode Learning Vector Quantization (LVQ) untuk mementukan gangguan kehamilan trisemester I,” Jurnal Sains, teknologi, dan industri , vol. 15, no. 2, pp. 144-151, 2 juni 2018.

T. Oktavia, D.H.Satyareni dan E. Jannah, “Rancang Bangun Sistem Pakar untuk Mendiagnosis Gangguan Kepribadian Histerik Menggunakan Metode Certainty Factor,” Jurnal Ilmiah Teknologi Sistem Informasi, vol. 1, no. 1, pp. 15-23, Januari 2015.

S.A.Oyewole dan O.O.Olugbara, “Product Image Classification using Eigen Colour Feature with Ensemble Machine Learning,” Egyptian informatics Journal, vol. 19, no. 2, pp. 83-100, July 2018.

L. M. James dan J. Taylor,

“Impulsivity and Negative Emotionality Associated with Subtance use Problems and Cluster B Personality in College Students,” Addictive Behaviors, vol. 32, no. 4, pp. 714-727, 2007.

A. A. Pradika, J. Jusak dan J. Lemantara, “Sistem pakar untuk Mendiagnosis gangguan jiwa skizofrenia menggunakan metode fuzzy expert system (studi kasus rs.Jiwa Menur Surabaya),” Jurnal JSIKA, vol. 1, no. 1, 2012.

H.takizawa, T.Chida dan H.Kobayashi, “Evaluating Computational Performance of Backpropagation Learning on Graphics Hardware,” Electronic Notes in Theoretical Computer Science, vol. 225, pp. 379-389, 2008.

Article Metrics

Abstract view(s): 120 time(s)
PDF: 59 time(s)

Refbacks

  • There are currently no refbacks.