Article Sidebar

Download
PDF
Statistic
Read Counter : 11 Download : 5

Downloads

Download data is not yet available.

Main Article Content

Abstract

Purpose: This study aims to fill this gap by evaluating the benefits and challenges of applying CVT to a prototype monitoring camera, as well as developing an optimal implementation strategy.


Research Design and Methodology: Through a mixed-methods approach and black-box testing, the results show that the application of CVT has great potential to revolutionize industrial IMS, particularly in aspects such as real-time monitoring, visual data analysis, and decision-making.


Findings and Discussion: The preliminary results suggest that this technology can improve operational efficiency, accuracy, and safety, thereby enhancing productivity and cost efficiency.


Implications: This research explores the potential contribution of CVT to IMS in the industrial sector, focusing on the use of a prototype Radio Frequency Identification (RFID)-based monitoring camera, and examines its long-term implications.

Keywords

computer vision technology radio frequency identification information management systems prototype monitoring camera

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Ar-Rafi, A. R., Permana, E., & Okitasari, H. . (2025). Preliminary Study: RFID-Based Computer Vision Technology Prototype as Information Management Systems in the Logistics Industry. Atestasi : Jurnal Ilmiah Akuntansi, 8(2), 426–436. https://doi.org/10.57178/atestasi.v8i2.1646
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Alam, M. F., Katsikas, S., Beltramello, O., & Hadjiefthymiades, S. (2017). Augmented and virtual reality-based monitoring and safety system: A prototype IoT platform. Journal of Network and Computer Applications, 89, 109–119. https://doi.org/10.1016/j.jnca.2017.03.022
  2. Almeida, F. (n.d.). European Journal of Education: Strategies for Conducting a Mixed Methods Study. https://doi.org/10.5281/zenodo.1406214
  3. Astuti, E. R. (2022). Improvement Quality of Software Requirements Using Requirement Negotiation System for Supporting Decision (Vol. 10, Issue 1).
  4. Barlow, S. E., O’Neill, M. A., & Pavlik, B. M. (2019). A prototype RFID tag for detecting bumblebee visitations within fragmented landscapes. Journal of Biological Engineering, 13(1). https://doi.org/10.1186/s13036-019-0143-x
  5. Bit, J., Tarigan, M., Kom, M., & Handayani, D. (2019). Prototype Pengembangan Sistem Pencatatan Stok Barang Dengan Teknologi RFID (Vol. 16, Issue 2). https://journal.budiluhur.ac.id/index.php/bit
  6. Chan, C. C., Abu Bakar, N. H., Raju, C. M., & Urban, P. L. (2024). Computer Vision-Assisted Robotized Sampling of Volatile Organic Compounds. Analytical Chemistry, 96(41), 16307–16314. https://doi.org/10.1021/acs.analchem.4c03361
  7. Cui, L., Zhang, Z., Gao, N., Meng, Z., & Li, Z. (2019). Radio frequency identification and sensing techniques and their applications—A review of the state-of-the-art. In Sensors (Switzerland) (Vol. 19, Issue 18). MDPI AG. https://doi.org/10.3390/s19184012
  8. Ekanayake, B., Wong, J. K. W., Fini, A. A. F., & Smith, P. (2021). Computer vision-based interior construction progress monitoring: A literature review and future research directions. In Automation in Construction (Vol. 127). Elsevier B.V. https://doi.org/10.1016/j.autcon.2021.103705
  9. Ekanem, I. I., Ohwoekevwo, J. U., & Ikpe, A. E. (2024). Conjectures of computer vision technology (CVT) on industrial information management systems (IMSs): a futuristic gaze. In Metaheuristic algorithms with applications (Vol. 1, Issue 1). http://creativecommons.org/licenses/by/4.0
  10. Gortschacher, L. J., & Grosinger, J. (2019). UHF RFID Sensor System Using Tag Signal Patterns: A Prototype System. IEEE Antennas and Wireless Propagation Letters, 18(10), 2209–2213. https://doi.org/10.1109/LAWP.2019.2940336
  11. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5). https://doi.org/10.1145/3236009
  12. Ismail, H., & Hanafiah, M. M. (2021). Evaluation of e-waste management systems in Malaysia using life cycle assessment and material flow analysis. Journal of Cleaner Production, 308. https://doi.org/10.1016/j.jclepro.2021.127358
  13. Kumar, M., Professor, A., Kumar Singh, S., Dwivedi, R. K., & Professor, A. (2015). International Journal of Advance Research in Computer Science and Management Studies. International Journal of Advance Research in Computer Science and Management Studies, 3(10). www.ijarcsms.com
  14. Loyola-Gonzalez, O. (2019). Black-box vs. White-Box: Understanding their advantages and weaknesses from a practical point of view. In IEEE Access (Vol. 7, pp. 154096–154113). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2019.2949286
  15. Markic, D. N., Carapina, H. S., Bjelic, D., Bjelic, L. S., Ilic, P., Pesic, Z. S., & Kikanovicz, O. (2019). Using material flow analysis for waste management planning. Polish Journal of Environmental Studies, 28(1), 255–265. https://doi.org/10.15244/pjoes/78621
  16. Nidhra, S. (2012). Black Box and White Box Testing Techniques - A Literature Review. International Journal of Embedded Systems and Applications, 2(2), 29–50. https://doi.org/10.5121/ijesa.2012.2204
  17. Oktaviani, L., Fernando, Y., Romadhoni, R., & Noviana, N. (2021). Developing a web-based application for school councelling and guidance during COVID-19 Pandemic. Journal of Community Service and Empowerment, 2(3), 110–117. https://doi.org/10.22219/jcse.v2i3.17630
  18. Pirdaus, D. I., & Hidayana, R. A. (2024). Analysis Testing Black Box and White Box on Application To-Do List Based Web. International Journal of Mathematics, Statistics, and Computing, 2(2), 68–75.
  19. Schandl, H., Fischer-Kowalski, M., West, J., Giljum, S., Dittrich, M., Eisenmenger, N., Geschke, A., Lieber, M., Wieland, H., Schaffartzik, A., Krausmann, F., Gierlinger, S., Hosking, K., Lenzen, M., Tanikawa, H., Miatto, A., & Fishman, T. (2018). Global material flows and resource productivity forty years of evidence. Journal of Industrial Ecology, 22(4), 827–838. https://doi.org/10.1111/jiec.12626
  20. Selvaraj, A. S., & Anusha, S. (2021). RFID enabled smart data analysis in a smart warehouse monitoring system using iot. Journal of Physics: Conference Series, 1717(1). https://doi.org/10.1088/1742-6596/1717/1/012022
  21. Sjödin, D. R., Parida, V., Leksell, M., & Petrovic, A. (2018). Smart Factory Implementation and Process Innovation: A Preliminary Maturity Model for Leveraging Digitalization in ManufacturingMoving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, processes, and technologies. Research Technology Management, 61(5), 22–31. https://doi.org/10.1080/08956308.2018.1471277
  22. Taherdoost, H. (2022). What are Different Research Approaches? Comprehensive Review of Qualitative, Quantitative, and Mixed Method Research, Their Applications, Types, and Limitations. Journal of Management Science & Engineering Research, 2022(1), 53–63. https://doi.org/10.30564/jmser.v5i1.4538ï
  23. Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020). Computer vision technology in agricultural automation —A review. In Information Processing in Agriculture (Vol. 7, Issue 1, pp. 1–19). China Agricultural University. https://doi.org/10.1016/j.inpa.2019.09.006
  24. Triki-Lahiani, A., Bennani-Ben Abdelghani, A., & Slama-Belkhodja, I. (2018). Fault detection and monitoring systems for photovoltaic installations: A review. In Renewable and Sustainable Energy Reviews (Vol. 82, pp. 2680–2692). Elsevier Ltd. https://doi.org/10.1016/j.rser.2017.09.101
  25. Ullo, S. L., & Sinha, G. R. (2020). Advances in smart environment monitoring systems using iot and sensors. In Sensors (Switzerland) (Vol. 20, Issue 11). MDPI AG. https://doi.org/10.3390/s20113113
  26. Veena, S., Vinoth, N. A. S., Nancy, A. M., Kumar, G. S., & Teja, R. T. (2020). Effective system for software requirement management. AIP Conference Proceedings, 2277. https://doi.org/10.1063/5.0025463
  27. Verma, A., Khatana, A., & Chaudhary, S. (2017). A Comparative Study of Black Box Testing and White Box Testing. International Journal of Computer Sciences and Engineering, 5(12), 301–304. https://doi.org/10.26438/ijcse/v5i12.301304
  28. Walz, M., & Guenther, E. (2021). What effects does material flow cost accounting have for companies?: Evidence from a case studies analysis. Journal of Industrial Ecology, 25(3), 593–613. https://doi.org/10.1111/jiec.13064
  29. Wang, T., Hu, B., Chang, S., & Ding, L. (2018). Inventory inaccuracies and radio frequency identification technology: Risk analysis and coordination. Computers and Industrial Engineering, 125, 9–22. https://doi.org/10.1016/j.cie.2018.08.003
  30. Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N. L., Guo, M., Bianconi, G. M., Alahi, A., Lee, J., Campbell, B., Deru, K., Beninati, W., Fei-Fei, L., & Milstein, A. (2019). A computer vision system for deep learning-based detection of patient mobilization activities in the ICU. Npj Digital Medicine, 2(1). https://doi.org/10.1038/s41746-019-0087-z
  31. Yuan, J., Zhang, L., & Kim, C. S. (2023). Multimodal Interaction of MU Plant Landscape Design in Marine Urban Based on Computer Vision Technology. Plants, 12(7). https://doi.org/10.3390/plants12071431
  32. Zhang, M., Fan, J., Sharma, A., & Kukkar, A. (2022). Data mining applications in university information management system development. Journal of Intelligent Systems, 31(1), 207–220. https://doi.org/10.1515/jisys-2022-0006