Advancing Logistics Training with Virtual Reality: A Case Study of PT XYZ's Innovative Approach in Indonesia
Keywords:
Fisher Yates Shuffle, logistics, technology acceptance model, Unity, virtual realityAbstract
PT XYZ is an Indonesian pharmaceutical company that provides domestic and international health services. PT XYZ established an apprenticeship program to design and develop virtual reality (VR)-based logistics practice training modules in the past. On December 28, 2022, PT XYZ logistics employees evaluated the newly developed training module for logistics in order to provide feedback. The development of logistics training modules is predicated on issues regarding motion sickness and assessment criteria. One of the training assessment features was created using the Fisher– Yates Shuffle algorithm, which was implemented in a training module application that had been previously developed using the Unity game engine. As part of the developments, the training flow is being redesigned, practice assessment features and exams are being added, and a warehouse environment is being created. On June 8, 2023, the logistics training module was effectively revised and retested. On the basis of the trial's evaluation, it was determined that efforts to prevent motion sickness had varying effects on trial participants. It was also discovered that participants viewed the assessment feature as beneficial, but that it remained difficult to access. On the basis of the assessment of the technology acceptance model (TAM), which was attended by six participants, an acceptance rate of 67.5% was obtained for the perceived simplicity of use and 78.33% was obtained for the perceived usefulness.
Downloads
References
M. Wongso and W. Istiono, “Learn Muay Thai Basic Movement in Virtual Reality and Sattolo Shuffle Algorithm,” International Journal of Science, Technology & Management, vol. 4, no. 2, pp. 341–349, 2023, doi: 10.46729/ijstm.v4i2.759.
J. I. Montana et al., “The benefits of emotion regulation interventions in virtual reality for the improvement of wellbeing in adults and older adults: A systematic review,” Journal of Clinical Medicine, vol. 9, no. 2, 2020, doi: 10.3390/jcm9020500.
T. G. Plante, A. Aldridge, R. Bogden, and C. Hanelin, “Might virtual reality promote the mood benefits of exercise?,” Computers in Human Behavior, vol. 19, no. 4, pp. 495–509, 2003, doi: 10.1016/S0747-5632(02)00074-2.
J. Sampurna and W. Istiono, “Virtual Reality Game for Introducing Pencak Silat,” International Journal of Interactive Mobile Technologies, vol. 15, no. 1, pp. 199–207, 2021, doi: 10.3991/IJIM.V15I01.17679.
E. H. Au and J. J. Lee, “Virtual reality in education: a tool for learning in the experience age,” International Journal of Innovation in Education, vol. 4, no. 4, p. 215, 2017, doi: 10.1504/ijiie.2017.091481.
M. Barreda-Ángeles and T. Hartmann, “Psychological benefits of using social virtual reality platforms during the covid-19 pandemic: The role of social and spatial presence,” Computers in Human Behavior, vol. 127, no. December 2020, 2022, doi: 10.1016/j.chb.2021.107047.
C. Diels and P. A. Howarth, “Frequency characteristics of visually induced motion sickness,” Human Factors, vol. 55, no. 3, pp. 595–604, 2013, doi: 10.1177/0018720812469046.
H. K. Kim, J. Park, Y. Choi, and M. Choe, “Virtual reality sickness questionnaire (VRSQ): Motion sickness measurement index in a virtual reality environment,” Applied Ergonomics, vol. 69, no. October 2017, pp. 66–73, 2018, doi: 10.1016/j.apergo.2017.12.016.
T. K. Hazra and S. Bhattacharyya, “Image encryption by blockwise pixel shuffling using Modified Fisher Yates shuffle and pseudorandom permutations,” 7th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEEE IEMCON 2016, no. October, 2016, doi: 10.1109/IEMCON.2016.7746312.
A. Olu, “A Simulated Enhancement of Fisher-Yates Algorithm for Shuffling in Virtual Card Games using Domain-Specific Data Structures,” International Journal of Computer Applications, vol. 54, no. 11, pp. 975–8887, 2012.
I. Febriani, R. Ekawati, U. Supriadi, and M. I. Abdullah, “Fisher-Yates shuffle algorithm for randomization math exam on computer based-test,” AIP Conference Proceedings, vol. 2331, no. April, 2021, doi: 10.1063/5.0042534.
T. F. Revano, M. B. Garcia, B. G. M. Habal, J. O. Contreras, and J. B. R. Enriquez, “Logical guessing riddle mobile gaming application utilizing fisher yates algorithm,” 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2018, pp. 1–4, 2019, doi: 10.1109/HNICEM.2018.8666302.
M. Masrom, “Technology acceptance model and E-learning,” 12th International Conference on Education, no. May, pp. 21–24, 2007.
R. J. Holden and B. T. Karsh, “The Technology Acceptance Model: Its past and its future in health care,” Journal of Biomedical Informatics, vol. 43, no. 1, pp. 159–172, 2010, doi: 10.1016/j.jbi.2009.07.002.
B. Keshavarz, B. E. Riecke, L. J. Hettinger, and J. L. Campos, “Vection and visually induced motion sickness: How are they related?,” Frontiers in Psychology, vol. 6, no. APR, pp. 1–11, 2015, doi: 10.3389/fpsyg.2015.00472.
L. J. Smart, T. A. Stoffregen, and B. G. Bardy, “Visually induced motion sickness predicted by postural instability,” Human Factors, vol. 44, no. 3, pp. 451–465, 2002, doi: 10.1518/0018720024497745.
J. E. Bos, S. C. de Vries, M. L. van Emmerik, and E. L. Groen, “The effect of internal and external fields of view on visually induced motion sickness,” Applied Ergonomics, vol. 41, no. 4, pp. 516–521, 2010, doi: 10.1016/j.apergo.2009.11.007.
I. Technology, “Implementation of the Fisher Yates Shuffle Algorithm in Medical Equipment Learning Applications with Augmented Reality Technology,” Journal of Computer Science, Information Technology and Telecommunication Engineering, vol. 3, no. 2, pp. 299–303, 2022, doi: 10.30596/jcositte.v3i2.11657.
F. Panca Juniawan, H. Arie Pradana, Laurentinus, and D. Yuny Sylfania, “Performance comparison of linear congruent method and fisher-yates shuffle for data randomization,” Journal of Physics: Conference Series, vol. 1196, no. 1, 2019, doi: 10.1088/1742-6596/1196/1/012035.
J. Li et al., “Networked human motion capture system based on quaternion navigation,” BodyNets International Conference on Body Area Networks, no. 1964, pp. 1–4, 2017, doi: 10.1145/0000000.0000000.
Molla, J. P., Dhabliya, D., Jondhale, S. R., Arumugam, S. S., Rajawat, A. S., Goyal, S. B., Suciu, G. (2023). Energy efficient received signal strength-based target localization and tracking using support vector regression. Energies, 16(1) doi:10.3390/en16010555
Robert Roberts, Daniel Taylor, Juan Herrera, Juan Castro, Mette Christensen. Enhancing Collaborative Learning through Machine Learning-based Tools. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/177
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
