A Lesson Learned from BSI Error Service: A Twitter Text Mining Data Approach Using NVivo

Nadia Nurul Izza, Evania Herindar, Nashr Akbar

Abstract

This research extensively examines Twitter trend data related to BSI (Bank Syariah Indonesia) error services, collecting 1,014 tweets in real-time using NCapture from NVivo 12 Plus within the timeframe of May 8-16, 2023. The key findings highlight that 81% of conversations regarding BSI error services carry a negative sentiment. Additionally, a word cloud identifies key terms such as "services" (2.21%), "transactions" (0.87%), "ransomware" (0.74%), and "trust" (0.42%). As a concrete solution, the research recommends enhancing the transparency of BSI management to the public and fostering close collaboration with relevant authorities such as OJK, BI, and LPS. Furthermore, the strengthening of BSI's cybersecurity is emphasized to mitigate risks. These findings not only provide in-depth insights but also present practical steps that can be implemented to protect consumers and restore public trust in the Islamic banking sector. As a valuable contribution to the literature, this research not only offers a snapshot of the current situation but also identifies relevant directions for future research. Overall, the study integrates comprehensive data analysis with impactful recommendations, establishing a robust foundation for understanding and continuous improvement in the management of BSI error services. Notably, this research is the first to use NVivo in collecting Twitter data related to BSI error services

Keywords

BSI, Service, Error, Twitter

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References

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DOI: http://dx.doi.org/10.21043/malia.v8i1.23436

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