Systematic Literature Review (SLR) pada Aplikasi Process Mining dalam Transformasi Digital Proses Bisnis

Authors

  • Yusran Panca Putra Universitas Bengkulu
  • Okka Adittio Putra
  • Willi Novrian

DOI:

https://doi.org/10.29240/arcitech.v5i1.13888

Keywords:

Systematic Literature Review, Process Mining, Digital Transformation, Business Process

Abstract

Digital transformation compels organizations to enhance the efficiency and transparency of their business processes. Process mining has emerged as a pivotal approach that leverages event logs from information systems to evaluate and improve processes in a data-driven manner. This study aims to conduct a Systematic Literature Review (SLR) of existing research exploring the application of process mining in supporting business process digital transformation. Relevant literature was retrieved from the Scopus database and filtered using rigorous inclusion and exclusion criteria, resulting in ten primary studies analyzed through the PICOS framework and PRISMA diagram. The findings indicate that process discovery, heuristic mining, and fuzzy mining are the most commonly employed techniques, with tools such as ProM and Disco frequently utilized. Research trends show increasing integration of process mining with artificial intelligence, simulation, and process automation technologies. However, several challenges remain, including limited data log quality and availability, lack of cross-system integration, and minimal validation in real-world settings. This study highlights the strategic role of process mining as a key enabler of digital transformation by enhancing efficiency, process visibility, and data-driven decision-making, while also providing a research landscape and future development directions in the domain.

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References

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Published

30-06-2025

How to Cite

Putra, Y. P., Okka Adittio Putra, & Willi Novrian. (2025). Systematic Literature Review (SLR) pada Aplikasi Process Mining dalam Transformasi Digital Proses Bisnis. Arcitech: Journal of Computer Science and Artificial Intelligence, 5(1), 90–107. https://doi.org/10.29240/arcitech.v5i1.13888

Issue

Vol. 5 No. 1 (2025): June 2025

Section

Articles

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Copyright (c) 2025 Yusran Panca Putra, Okka Adittio Putra, Willi Novrian

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