Development of an Interactive Virtual Reality for Medical Skills Training Supervised by Artificial Neural Network

Virtual reality (VR) is defined as a 3-dimensional (3D), artificially simulated environment which allows the user to immerse himself/herself in it. From rehabilitation to data visualization, VR has been found to have many profound applications over the past decade or so. The addition of a suitable interface (e.g: haptics) is necessary in order to improve the quality of interaction with VR. Artificial Neural Network (ANN), a learning algorithm [i.e., a mathematical representation of any form of biological activity], which is one of the most widely adopted algorithms, is used for maintaining the properties of virtual reality (i.e., Immersivity and Interactivity). The primary objective of this review is to explore the limitless possibilities through the integration of ANN and VR. In addition to this, it also highlights the fact that an incumbent association of VR and ANN can lead to the construction of a highly interactive and immersive module in virtual reality.

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Sistem Evaluasi Kelayakan Mahasiswa MagangMenggunakan Elman Recurrent Neural Network

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.3494 ◽

2014 ◽

Vol 8 (1) ◽

pp. 37 ◽

Cited By ~ 1

Author(s):

Agus Aan Jiwa Permana ◽

Widodo Prijodiprodjo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Recurrent Neural Network ◽

Skills Training ◽

Minimum Error ◽

Training Mode ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer ◽

Elman Recurrent Neural Network

AbstrakJaringan Syaraf Tiruan (JST) dapat digunakan untuk memecahkan permasalahan tertentu seperti prediksi, klasifikasi, pengolahan data, dan robotik.Berdasarkan paparan tersebut, sehingga dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam program magang yang sedang dihadapi dalam upaya untuk meningkatkan kompetensi, pengalaman, serta melatih softskill mahasiswa.Sistem yang dikembangkan dapat digunakan untuk mengevaluasi kelayakan mahasiswa dalam program magang ke luar daerah dengan menerapkan Elman Recurrent Neural Network (ERNN), sehingga dapat memberikan informasi yang akurat kepada pihak jurusan untuk menentukan keputusan yang tepat.Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi. Adapun metode pembelajaran yang digunakan adalah Backpropagation ThroughTime dengan model epochwise training mode. Sistem diimplementasikan dengan menggunakan bahasa pemrograman C# dengan basis data MySQL. Vektor input yang digunakan terdiri dari 11 variabel. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan akan cepat mengalami konvergen dan mampu mencapai nilai error paling optimal (minimum error) apabila menggunakan 1 hidden layer dengan jumlah neuron 20 unit. Akurasi terbaik dapat diperoleh dengan menggunakan LR sebesar 0.01 dan momentum 0.85 dimana akurasi rata-rata dalam pengujian mencapai 87.50%. Kata kunci—Evaluasi, Kelayakan, Jaringan Syaraf Tiruan (JST), Elman Recurrent Neural Network, Magang Abstract Artificial Neural Network (ANN) can be used to solve specific problems such as prediction, classification, data processing, and robotics. Based on the exposure, so in this study tried to apply neural networks to handle problems in apprentice program facing in an effort to increase the competence, experience and soft skills training students. The system developed can be used to evaluate the students in the apprentice program to other regions by applying the Elman Recurrent Neural Network (ERNN), so it can provide accurate information to the department to determine appropriate decisions. Elman structure was chosen because it can be create much more rapidly iterations so as to facilitate the convergence process. The learning method used is Backpropagation Through Time with model epochwise training mode. The system is implemented using the C # programming language with a MySQL database. Input vector used consists of 11 variables. The results showed that the developed system will rapidly converge and can reach optimal error value (minimum error) when using one hidden layer with 20 units number of neurons. Best accuracy can be obtained using the LR of 0.01 and momentum 0.85 which average accuracy reaches 87.50% in testing. Keywords—Evaluation, Feasibility, Artificial Neural Network (ANN), Elman Recurrent Neural Network, Apprenticeship

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