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dc.contributor.authorHermawan, Arief
dc.date.accessioned2012-08-10T03:33:25Z
dc.date.available2012-08-10T03:33:25Z
dc.date.issued2009-08-17
dc.identifier.citationDemuth, H dan Beale, D, (1995), “Neural Network Toolbox for Use with Matlab”, the Math Work, Inc., Massachussetts. Fausettt, L., (1994), “Fundamentals of Neural Network, Architecture, Algoritm and Application”, Printice-Hall, Inc., London Hair, JF., Anderson, RE., Tatham, RL., dan Black, WC., (1998), “Multivariate Data Analysis”, Printice Hall, Inc., Ner Jersey Hermawan, Arief, (2006), “Jaringan Saraf Tiruan, Teori dan Aplikasi”, Andi Ofset, Yogyakarta Hu, Y C., dan Tseng, FM (2005), “Applying Backpropagation Neural Network to Bankruptcy prediction”, International Journal of Electronic Business Management, Vol. 3, No. 2, pp. 97-103 Wilson, RL., dan Sharda, R., (1994), “Bankrupcy Prediction Using Neural Network”, Decision Support System, 11, hal 545-557 Wibowo, AP, (2009), “Sistem Identifikasi Potensi Satpam”, Tugas Akhir, Teknik Informatika-S1 Universitas Teknologi Yogyakarta, Yogyakartaen_US
dc.identifier.issn1412-9612
dc.identifier.urihttp://hdl.handle.net/11617/1847
dc.description.abstractDiscriminant analysis is a statistical technique that uses information available in a set of independent variables to classify the value of a descrete or categorical dependent variable. Depending on the research that have been done discriminant analysis can not classify the pattern with perfect accuracy (100%). This study aimed to improve the accuracy of discriminat analysis in classificationing the security potencial with neural network. This study will show the comparation between neural network and discriminant analysis in their work on classifiction the security potencial. The security potencial will be measured with capability, behavior, and skill variable. Three layer neural network was trained with the back propagation algorithm, input layer consist of 3 neurons, hidden layer consist of 60 neurons and output layer consist of 1 neuron, the value of momentum was 0,6 dan the value of initial learning rate was 0,4. After the simulation of neural network was done, the reseacher find that neural network has a better work product than analisys discriminant in classificationing the security potencial.en_US
dc.publisherFT-LPPM UMSen_US
dc.subjectneural networken_US
dc.subjectdiscriminant analysisen_US
dc.subjectsecurity potencialen_US
dc.titlePERBAIKAN KEAKURATAN KLASIFIKASI POTENSI SATPAM DENGAN MENGGUNAKAN JARINGAN SARAF TIRUAN TERHADAP METODE ANALISIS DISKRIMINAN KLASIKen_US
dc.typeArticleen_US


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