Article Sidebar

Download
PDF
Statistic
Read Counter : 88 Download : 77

Downloads

Download data is not yet available.

Main Article Content

Abstract

Tujuan: Penelitian ini bertujuan untuk mengeksplorasi peran big data dalam meningkatkan akurasi dan efisiensi penilaian risiko oleh akuntan modern, serta mengidentifikasi tantangan dan peluang strategis dalam implementasinya di era digital. Studi ini berangkat dari asumsi bahwa integrasi teknologi canggih dapat memperkuat fungsi akuntansi sebagai alat pengambilan keputusan yang berbasis data.


Desain Penelitian dan Metodologi: Penelitian ini menggunakan pendekatan Systematic Literature Review (SLR) yang disusun berdasarkan protokol PRISMA. Sebanyak 21 artikel ilmiah dari jurnal internasional bereputasi dianalisis secara tematik. Kriteria inklusi mencakup publikasi setelah tahun 2018 yang membahas hubungan antara big data dan penilaian risiko dalam konteks akuntansi.


Hasil dan Pembahasan: Hasil kajian menunjukkan bahwa teknologi seperti regional computing, parallel computing, dan mobile edge computing secara signifikan meningkatkan kecepatan dan kualitas proses analisis risiko. Namun, tantangan utama meliputi keterbatasan infrastruktur teknologi, kurangnya kompetensi analitik di kalangan akuntan, serta isu etika dan tata kelola data.


Implikasi: Penelitian ini menegaskan pentingnya peningkatan kapasitas profesional akuntansi dalam literasi digital dan teknologi. Selain itu, studi ini merekomendasikan riset lanjutan terkait strategi penerapan big data yang adil dan berkelanjutan dalam manajemen risiko akuntansi.

Keywords

big data penilaian risiko akuntansi digital regional computing audit berbasis data

Article Details

Creative Commons License

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

How to Cite
Khaerunnisa, A., Karfina AR, L., Amiruddin, A., & Syamsuddin, S. (2025). Integrasi Big Data dalam Penilaian Risiko: Tantangan dan Peluang bagi Akuntan Modern. Atestasi : Jurnal Ilmiah Akuntansi, 8(2), 102–114. https://doi.org/10.57178/atestasi.v8i2.1418
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Abbak, R. A., & Ustun, A. (2015). A software package for computing a regional gravimetric geoid model by the KTH method. Earth Science Informatics, 8(1), 255–265. https://doi.org/10.1007/s12145-014-0149-3
  2. Ali, O., Soar, J., & Shrestha, A. (2018). Perceived potential for value creation from cloud computing: a study of the Australian regional government sector. Behaviour and Information Technology, 37(12), 1157–1176. https://doi.org/10.1080/0144929X.2018.1488991
  3. Ali, O., Soar, J., & Yong, J. (2016). An investigation of the challenges and issues influencing the adoption of cloud computing in Australian regional municipal governments. Journal of Information Security and Applications, 27–28, 19–34. https://doi.org/10.1016/j.jisa.2015.11.006
  4. Asrul, A. (2024). Transpormasi Bisnis Di Era Digital: Peluang, Tantangan, Dan Strategi Inovasi. Jurnal Minfo Polgan, 13(2), 2294–2298. https://doi.org/10.33395/jmp.v13i2.14431
  5. Badshah, A., Daud, A., Alhajlah, M., Alsahfi, T., Alshemaimri, B., & Ghani-Ur-Rehman. (2024). Smart Cities’ Big Data: Performance and Cost Optimization with Regional Computing. IEEE Access, 12(August), 1–1. https://doi.org/10.1109/access.2024.3457269
  6. Badshah, A., Iwendi, C., Jalal, A., Hasan, S. S. U., Said, G., Band, S. S., & Chang, A. (2022). Use of regional computing to minimize the social big data effects. Computers and Industrial Engineering, 171(July), 108433. https://doi.org/10.1016/j.cie.2022.108433
  7. Bakri, A., Yusni, Y., & Botutihe, N. (2023). Analisis Efektivitas Penggunaan Teknologi Big Data dalam Proses Audit: Studi Kasus pada Kantor Akuntan Publik di Indonesia. Jurnal Akuntansi Dan Keuangan West Science, 2(03), 179–186. https://doi.org/10.58812/jakws.v2i03.641
  8. Balci, G., & Ali, S. I. (2024). The relationship between information processing capabilities, Net-Zero capability and supply chain performance. Supply Chain Management: An International Journal, 29(2), 351–370. https://doi.org/10.1108/SCM-06-2023-0320
  9. Budi, B. N., & Lestari, R. P. (2022). Keadaan Big Data dapat menjadi Peluang dan Tantangan Bagi Good Governance di Kemajuan Sosial. Al-Mubin: Islamic Scientific Journal, 5(1), 10–17. https://doi.org/10.51192/almubin.v5i01.245
  10. Chang, F.-J., Chang, L.-C., Huang, C.-W., & Kao, I.-F. (2016). Prediction of monthly regional groundwater levels through hybrid soft-computing techniques. Journal of Hydrology, 541, 965–976. https://doi.org/https://doi.org/10.1016/j.jhydrol.2016.08.006
  11. Chen, Y., Sun, Y., Feng, T., & Li, S. (2020). A Collaborative Service Deployment and Application Assignment Method for Regional Edge Computing Enabled IoT. IEEE Access, 8, 112659–112673. https://doi.org/10.1109/ACCESS.2020.3002813
  12. Geschke, A., Ugon, J., Lenzen, M., Kanemoto, K., & Moran, D. D. (2019). Balancing and reconciling large multi-regional input–output databases using parallel optimisation and high-performance computing. Journal of Economic Structures, 8(1). https://doi.org/10.1186/s40008-019-0133-7
  13. Jang, W. S., Lee, Y., Neff, J. C., Im, Y., Ha, S., & Doro, L. (2019). Development of an EPIC parallel computing framework to facilitate regional/global gridded crop modeling with multiple scenarios: A case study of the United States. Computers and Electronics in Agriculture, 158(February), 189–200. https://doi.org/10.1016/j.compag.2019.02.004
  14. Kaloop, M. R., Rabah, M., Hu, J. W., & Zaki, A. (2018). Using advanced soft computing techniques for regional shoreline geoid model estimation and evaluation. Marine Georesources and Geotechnology, 36(6), 688–697. https://doi.org/10.1080/1064119X.2017.1370622
  15. Kanaparthi, V. (2024). Exploring the Impact of Blockchain, AI, and ML on Financial Accounting Efficiency and Transformation BT - Multi-Strategy Learning Environment (V. Vimal, I. Perikos, A. Mukherjee, & V. Piuri (eds.); pp. 353–370). Springer Nature Singapore.
  16. Kassem, R. (2024). External auditors’ use and perceptions of fraud factors in assessing fraudulent financial reporting risk (FFRR): Implications for audit policy and practice. Security Journal, 37(3), 875–902. https://doi.org/10.1057/s41284-023-00399-w
  17. Kumar, R., Goel, N. K., Chatterjee, C., & Nayak, P. C. (2015). Regional Flood Frequency Analysis using Soft Computing Techniques. Water Resources Management, 29(6), 1965–1978. https://doi.org/10.1007/s11269-015-0922-1
  18. Kusakabe, R., Fujita, K., Ichimura, T., Yamaguchi, T., Hori, M., & Wijerathne, L. (2021). Development of regional simulation of seismic ground‐motion and induced liquefaction enhanced by GPU computing. Earthquake Engineering & Structural Dynamics, 50(1), 197–213. https://doi.org/10.1002/eqe.3369
  19. Mais, R., Wulaningsih, R., Oktasari, E., Setiawan, D., & Wulandari, W. (2025). Artificial Intelligence (AI) dalam Akuntansi: Peluang dan Tantangan untuk Profesi Akuntan. Jesya (Jurnal Ekonomi Dan Ekonomi Syariah), 8(1 SE-Articles). https://doi.org/https://doi.org/10.36778/jesya.v8i1.1976
  20. Marr, B. (2021). Data strategy: How to profit from a world of big data, analytics and artificial intelligence. Kogan Page Publishers.
  21. Moschidis, S., Drogalas, G., Chatzipetrou, E., & Lois, P. (2024). An investigation of risk-based auditing (RBA) relationships from the stakeholders’ perspective using PLS-SEM. EuroMed Journal of Business, ahead-of-print(ahead-of-print). https://doi.org/10.1108/EMJB-12-2022-0211
  22. Nurina, L., Sudarmanto, E., Susanto, E., Utami, R., & Ananda, S. (2024). Integrasi Big Data dan Kecerdasan Buatan: Potensi dan Tantangan Menurut Tinjauan Literatur Sistematis. Nusantara Computer and Design Review, 2(1), 1–6. https://doi.org/10.55732/ncdr.v2i1.1204
  23. Otero González, L., Durán Santomil, P., & Tamayo Herrera, A. (2020). The effect of Enterprise Risk Management on the risk and the performance of Spanish listed companies. European Research on Management and Business Economics, 26(3), 111–120. https://doi.org/https://doi.org/10.1016/j.iedeen.2020.08.002
  24. Pazouki, S., Jamshidi, M. B., Jalali, M., & Tafreshi, A. (2025). The integration of big data in Fintech: review of enhancing financial services through advanced technologies. World Journal of Advanced Research and Reviews, 25(1). https://doi.org/10.30574/wjarr.2025.25.1.0060
  25. Ponti, L., Gutierrez, A. P., Giannakopoulos, C., Varotsos, K. V, López Nevado, J., López Feria, S., Rivas González, F. W., Caboni, F., Stocchino, F., Rosati, A., Marchionni, D., Cure, J. R., Rodríguez, D., Terrado, M., De Felice, M., Dell’Aquila, A., Calmanti, S., Arjona, R., & Sanderson, M. (2024). Prospective regional analysis of olive and olive fly in Andalusia under climate change using physiologically based demographic modeling powered by cloud computing. Climate Services, 34, 100455. https://doi.org/https://doi.org/10.1016/j.cliser.2024.100455
  26. Sawadogo, P., & Darmont, J. (2021). On data lake architectures and metadata management. Journal of Intelligent Information Systems, 56(1), 97–120. https://doi.org/10.48550/arXiv.2107.11152
  27. Setiawati, E., Rohmah, S., & Yanti, N. (2024). Tantangan Profesi Akuntan Di Era Society 5.0;‎ Integrasi Inovasi Artificial Intelligence (Ai) Dan‎ Internet Of Things (LOT) Dalam Akuntansi. Jurnal GeoEkonomi, 15(1.2024), 31–40. https://doi.org/https://doi.org/10.36277/geoekonomi.v15i1.2024.447
  28. Sukma, E., Risdayani, N., & Hendra, J. (2025). Peran Digitalisasi Dalam Meningkatkan Efektivitas Manajemen Risiko Lembaga Keuangan Syariah. Journal of Business Inflation Management and Accounting, 2(1), 41–48. https://doi.org/10.57235/bima.v2i1.4528
  29. Sun, Y., Li, J., Lu, M., & Guo, Z. (2024). Study of the Impact of the Big Data Era on Accounting and Auditing. ArXiv Preprint ArXiv:2403.07180. https://doi.org/10.48550/arXiv.2403.07180
  30. Tillema, S., Trapp, R., & van Veen-Dirks, P. (2022). Business Partnering in Risk Management: A Resilience Perspective on Management Accountants’ Responses to a Role Change*. Contemporary Accounting Research, 39(3), 2058–2089. https://doi.org/10.1111/1911-3846.12774
  31. Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233. https://doi.org/10.1038/s41467-019-14108-y
  32. Vinuesa, R., & Sirmacek, B. (2021). Interpretable deep-learning models to help achieve the Sustainable Development Goals. Nature Machine Intelligence, 3(11), 926. https://doi.org/10.48550/arXiv.2108.10744
  33. Wang, G., & Xu, F. (2020). Regional intelligent resource allocation in mobile edge computing based vehicular network. IEEE Access, 8, 7173–7182. https://doi.org/10.1109/ACCESS.2020.2964018
  34. Wang, J., Zhang, W., Shi, Y., Duan, S., & Liu, J. (2018). Industrial big data analytics: challenges, methodologies, and applications. ArXiv Preprint ArXiv:1807.01016. https://doi.org/10.48550/arXiv.1807.01016
  35. Wang, X., Ferreira, F. A. F., & Yan, P. (2025). A multi-objective optimization approach for integrated risk-based internal audit planning. Annals of Operations Research, 346(2), 1811–1840. https://doi.org/10.1007/s10479-023-05228-2
  36. Wu, H., Zhang, L., & Zhang, X. (2019). Cloud Data and Computing Services Allow Regional Environmental Assessment: A Case Study of Macquarie-Castlereagh Basin, Australia. Chinese Geographical Science, 29(3), 394–404. https://doi.org/10.1007/s11769-019-1040-4
  37. Xu, J., Xiao, Y., Xie, G., Wang, Y., & Jiang, Y. (2019). Computing payments for wind erosion prevention service incorporating ecosystem services flow and regional disparity in Yanchi County. Science of The Total Environment, 674, 563–579. https://doi.org/https://doi.org/10.1016/j.scitotenv.2019.03.361
  38. Yao, L., Xu, J., Zhang, L., Pang, Q., & Zhang, C. (2019). Temporal-spatial decomposition computing of regional water intensity for Yangtze River Economic Zone in China based on LMDI model. Sustainable Computing: Informatics and Systems, 21, 119–128. https://doi.org/https://doi.org/10.1016/j.suscom.2018.11.008
  39. Yu, N., & Haskins, T. (2021). Bagging machine learning algorithms: A generic computing framework based on machine-learning methods for regional rainfall forecasting in upstate new york. Informatics, 8(3). https://doi.org/10.3390/informatics8030047
  40. Yue, J. (2021). RETRACTED ARTICLE: Big data-based twin network target tracking and local regional economic development. Personal and Ubiquitous Computing, 25(S1), 43–43. https://doi.org/10.1007/s00779-021-01640-2
  41. Zhang, P., Cao, W., & Muccini, H. (2020). Quality assurance technologies of big data applications: A systematic literature review. ArXiv Preprint ArXiv:2002.01759. https://doi.org/10.48550/arXiv.2002.01759
  42. Zhao, B., Liu, M., Wu, J., Liu, X., Liu, M., & Wu, L. (2020). Parallel Computing for Obtaining Regional Scale Rice Growth Conditions Based on WOFOST and Satellite Images. IEEE Access, 8, 223675–223685. https://doi.org/10.1109/ACCESS.2020.3043003