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All Journal JOIV : International Journal on Informatics Visualization
Fajrianti, Evianita Dewi
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Computer Science & IT

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Implementation of Virtual Fiber Optic Module Using Virtual Reality for Vocational Telecommunications Students Sukaridhoto, Sritrusta; Fajrianti, Evianita Dewi; Haz, Amma Liesvarastranta; Budiarti, Rizqi Putri Nourma; Agustien, Lusiana
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1361

Abstract

Virtual Reality (VR) technology is a computer technology capable of replicating a real environment into an immersive world. VR is also capable of simulating the user's physical condition so that they are able to interact. In the world of education as a support for educational activities, especially for vocational high school students who are more dominant in practicum, this study aims to show the results of the early stages of implementing Fiber Optic (FO) learning support modules. The module development process is carried out in several stages: scenario preparation, 3D asset development with low poly optimization, scenario integration with 3D assets, triggering every interaction, multiuser settings, and implementation. To build a good VR Fiber Optic application, measurements using OVR metrics are carried out. This measurement aims to determine the reliability of the application that affects the user experience. In the OVR metric measurement, the highest FPS value is 43, and the lowest FPS value is 27. The value obtained is good enough to run VR applications with low poly asset composition. Measurement of the level of user satisfaction is also carried out by using the PIECES Framework. From the measurement results, the value of each variable is in the range of 3.4 - 4.91, which means that users are satisfied with the VR FO application as a virtual learning support module. This research shows that Virtual Reality technology can provide an overview of practicum that is easy to access and not limited by space and material.
High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease Fajrianti, Evianita Dewi; Pratama, Afis Asryullah; Nasyir, Jamal Abdul; Rasyid, Alfandino; Winarno, Idris; Sukaridhoto, Sritrusta
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.793

Abstract

In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system.
Co-Authors Agustien, Lusiana Budiarti, Rizqi Putri Nourma Haz, Amma Liesvarastranta Idris Winarno Nasyir, Jamal Abdul Pratama, Afis Asryullah Rasyid, Alfandino