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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) DINAMIKA DOTCOM Teknik dan Ilmu Komputer Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Teknik Informatika (JUTIF)
Cynthia Hayat
Universitas Kristen Krida Wacana - Jakarta Barat
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Journal : Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika

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ARCHITECTURE OF BACK PROPAGATION NEURAL NETWORK MODEL FOR EARLY DETECTION OF TENDENCY TO TYPE B PERSONALITY DISORDERS Hayat, Cynthia; Limong, Samuel; Sagala, Noviyanti
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 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%.
Virtual Reality Visualization of Tongkonan Traditional House as Promotional Media for Cultural Tourism using ADDIE Model Hayat, Cynthia; Panggeso, David
Khazanah Informatika Vol. 6 No. 2 October 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i2.11049

Abstract

Indonesia is a country that is rich in culture and customs. Indonesia also has great potential in the field of tourism, especially cultural tourism. The key to attracting tourist visitors lies in the information media provided. In this paper, a desktop-based application is developed with 3D virtual reality graphics model technology with Tongkonan traditional house from the Toraja tribe and their environment as the object. This 3D virtual reality visualization aims to be an interactive promotional media for the millennial generation in introducing cultural tourism, especially the Tongkonan traditional house of Toraja. In this application, the user can explore the traditional house objects as a whole – with the help of navigation control in the form of keyboard and mouse. The ADDIE model method was used in designing this desktop application. The user response test was used to measure respondents' attitudes toward the application using the Likert scale and succeeded in getting the very good category in 17 questionnaire statements and the good category in three questionnaire statements. Therefore, it can be concluded that the VR visualization of Tongkonan Traditional House can act as an interactive promotional media to the millennial generation.
Co-Authors Afrido V. Hutapea Cancerita, Stefani Christian, Juan Christian, Neil Endi Putro Gisela Nina Sevani Iwan Aang Soenandi Jenny Rosenta Ulina Purba Limong, Samuel Marcel Marcel Mega Putri Oktaviani Mudita, Damida Shu Panggeso, David Perangin Angin, Prasasti Putratama, Andhika Sagala, Noviyanti Setiawan, Theodorous Stevanus Usen, Yolan Anjani Yudhi Windarto