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How should Bank Syariah Indonesia respond to cyber-attacks Churn, sentiments, and emotions analysis with machine learning
Submitted
December 16, 2023
Accepted
May 17, 2024
Published
June 28, 2024
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Abstract

Introduction
This study aims to identify the most popular topics and words in Twitter conversations regarding cyber-attacks on Bank Syariah Indonesia that occurred in May 2023. It also seeks to analyze the sentiments, emotions, and potential customer churn of netizens following cyber-attacks.
Objectives
The objective of this study is to investigate the public's response to cyber-attacks on Bank Syariah Indonesia, focusing on identifying key topics, analyzing sentiments and emotions, and estimating potential customer churn.
Method
This study uses a qualitative method with a sentiment analysis approach utilizing Orange Data Mining software. The data comprises tweets collected from May 10, 2023, to May 24, 2023, using keywords such as “BSI” and “Bank Syariah Indonesia,” resulting in 30,014 tweets. Sentiment and emotion analyses were conducted to categorize tweets and identify the prevalent sentiments and emotions.
Results
The analysis reveals that the words “BSI,” “Data,” and “Lockbit” are most frequently mentioned, indicating the relevance of the cyber-attackers who targeted Bank Syariah Indonesia. The sentiment analysis showed that 56% of the tweets were neutral and dominated by emotions of joy. The study also identifies a short-term potential churn rate of 1.60% for Bank Syariah Indonesia's total customer base, indicating the risk of customers switching to other banks.
Implications
The results highlight the importance of robust cybersecurity measures and quick response strategies for maintaining customer trust and satisfaction. Financial institutions, particularly banks, must prioritize information and technology security to prevent customer churn and ensure the continuity of their services.
Originality/Novelty
This study provides insights into public reactions to cyber-attacks on Islamic banks, emphasizing the role of sentiment and emotion analysis in understanding customer behavior. This offers practical implications for improving risk management and customer retention strategies in the banking sector.

Keywords

Bank Syariah Indonesia churn analysis cyber-attacks emotions analysis machine learning sentiments analysis Twitter

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Copyright (c) 2024 Yan Putra Timur, Ahmad Ajib Ridlwan, Khusnul Fikriyah, Fitriyah Dwi Susilowati, Clarashinta Canggih, Fira Nurafini

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How to Cite
Timur, Y. P., Ridlwan, A. A., Fikriyah, K., Susilowati, F. D. ., Canggih, C., & Nurafini, F. . (2024). How should Bank Syariah Indonesia respond to cyber-attacks? Churn, sentiments, and emotions analysis with machine learning. Journal of Islamic Economics Lariba, 10(1), 439–470. https://doi.org/10.20885/jielariba.vol10.iss1.art24
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References

  1. Abigail, P. Y. D. (2023, February 22). Aset BSI terbesar keenam di Indonesia, ini sederet faktanya [BSI’s sixth largest asset in Indonesia, here are some facts] [HTML]. Katadata.co.id. https://katadata.co.id/finansial/keuangan/63f60b6e1938d/aset-bsi-terbesar-keenam-di-indonesia-ini-sederet-faktanya

  2. Adelmann, F., Elliott, J., Ergen, I., Gaidosch, T., Jenkinson, N., Tanai Khiaonarong, Morozova, A., Schwarz, N., & Wilson, C. (2020). Cyber risk and financial stability: It’s a small world after all. International Monetary Fund. https://doi.org/10.5089/9781513512297.006

  3. Ahmed, S., Mohiuddin, M., Rahman, M., Tarique, K. M., & Azim, Md. (2022). The impact of Islamic Shariah compliance on customer satisfaction in Islamic banking services: Mediating role of service quality. Journal of Islamic Marketing, 13(9), 1829–1842. https://doi.org/10.1108/JIMA-11-2020-0346

  4. Ajike, E. O., Omoduemuke, N., & Adeoye, S. O. (2024). Enhancing competitive advantage: The role of self-efficacy in addressing customer loyalty and delivery dependability. International Journal of Strategic Research in Education Technology and Humanities, 12(1), 124–142. https://doi.org/10.48028/iiprds/ijsreth.v12.i1.08

  5. Al-Dulaimi, M. K. H., Al-Dulaimi, A. M. K., & Al-Dulaimi, O. M. K. (2022). Security measures of protection for banking systems. 2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), 597–601. https://doi.org/10.1109/PICST57299.2022.10238672

  6. Alimolaei, S. (2015). An intelligent system for user behavior detection in Internet Banking. 2015 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 1–5. https://doi.org/10.1109/CFIS.2015.7391642

  7. Amin, M., Isa, Z., & Fontaine, R. (2013). Islamic banks: Contrasting the drivers of customer satisfaction on image, trust, and loyalty of Muslim and non‐Muslim customers in Malaysia. International Journal of Bank Marketing, 31(2), 79–97. https://doi.org/10.1108/02652321311298627

  8. Antonakaki, D., Fragopoulou, P., & Ioannidis, S. (2021). A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks. Expert Systems with Applications, 164, 114006. https://doi.org/10.1016/j.eswa.2020.114006

  9. Antonio, M. S., Sanrego, Y. D., & Taufiq, M. (2012). An analysis of Islamic banking performance: Maqashid Index Implementation in Indonesia and Jordania. Journal of Islamic Finance, 1(1), 2–29. https://doi.org/10.31436/jif.v1i1.2

  10. Araminta, D. V., Qudziyah, Q., & Timur, Y. P. (2022). The role of green sukuk in realizing the sustainable development goals 2030 agenda. Jurnal Ekonomi Dan Bisnis Islam (Journal of Islamic Economics and Business), 8(2), 251–266. https://doi.org/10.20473/jebis.v8i2.37531

  11. Arenas-Gaitan, J., Peral-Peral, B., & Ramon-Jeronimo, M. A. (2015). Elderly and Internet Banking: An Application of UTAUT2. The Journal of Internet Banking and Commerce, 20(1), 1–23. https://www.icommercecentral.com/peer-reviewed/elderly-and-internet-banking-an-application-of-utaut2-50466.html

  12. Arfan, A., & Arfan, I. A. (2021). A strategy for strengthening public perception toward Sharia banking. Banks and Bank Systems, 16(2), 170–181. https://doi.org/10.21511/bbs.16(2).2021.16

  13. Azhagiri, M., Meena, S. D., Rajesh, A., Mangaleeswaran, M., & Sethupathi, M. G. (2023). Empirical study on sentiment analysis. Indian Journal of Artificial Intelligence and Neural Networking, 3(1), 8–18. https://doi.org/10.54105/ijainn.B1044.123122

  14. Bajwa, I. A., Ahmad, S., Mahmud, M., & Bajwa, F. A. (2023). The impact of cyberattacks awareness on customers’ trust and commitment: An empirical evidence from the Pakistani banking sector. Information & Computer Security, 31(5), 635–654. https://doi.org/10.1108/ICS-11-2022-0179

  15. Bank Indonesia. (2019). Blueprint sistem pembayaran indonesia 2025 [Blueprint of Indonesian payment system 2025]. Bank Indonesia. https://www.bi.go.id/id/fungsi-utama/sistem-pembayaran/blueprint-2025/default.aspx

  16. Barth, S., De Jong, M. D. T., Junger, M., Hartel, P. H., & Roppelt, J. C. (2019). Putting the privacy paradox to the test: Online privacy and security behaviors among users with technical knowledge, privacy awareness, and financial resources. Telematics and Informatics, 41, 55–69. https://doi.org/10.1016/j.tele.2019.03.003

  17. Bhattacharyya, J., & Dash, M. K. (2020). Investigation of customer churn insights and intelligence from social media: A netnographic research. Online Information Review, 45(1), 174–206. https://doi.org/10.1108/OIR-02-2020-0048

  18. Chakraborty, K., Bhattacharyya, S., & Bag, R. (2020). A survey of sentiment analysis from social media data. IEEE Transactions on Computational Social Systems, 7(2), 450–464. https://doi.org/10.1109/TCSS.2019.2956957

  19. Chan, H. K., Lacka, E., Yee, R. W. Y., & Lim, M. K. (2017). The role of social media data in operations and production management. International Journal of Production Research, 55(17), 5027–5036. https://doi.org/10.1080/00207543.2015.1053998

  20. Coussement, K. (2014). Improving customer retention management through cost-sensitive learning. European Journal of Marketing, 48(3/4), 477–495. https://doi.org/10.1108/EJM-03-2012-0180

  21. Damanhur, Albra, W., Syamni, G., & Habibie, M. (2018). What is the determinant of non-performing financing in branch Sharia regional bank in Indonesia. Emerald Reach Proceedings Series, 1, 265–271. https://doi.org/10.1108/978-1-78756-793-1-00081

  22. De Lima Lemos, R. A., Silva, T. C., & Tabak, B. M. (2022). Propension to customer churn in a financial institution: A machine learning approach. Neural Computing and Applications, 34(14), 11751–11768. https://doi.org/10.1007/s00521-022-07067-x

  23. Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., & Zupan, B. (2013). Orange: Data mining toolbox in Python. The Journal of Machine Learning Research, 14(1), 2349–2353. https://doi.org/10.5555/2567709.2567736

  24. DePaolo, C. A., & Wilkinson, K. (2014). Get your head into the clouds: Using word clouds for analyzing qualitative assessment data. TechTrends, 58(3), 38–44. https://doi.org/10.1007/s11528-014-0750-9

  25. Dhaoui, C., Webster, C. M., & Tan, L. P. (2017). Social media sentiment analysis: Lexicon versus machine learning. Journal of Consumer Marketing, 34(6), 480–488. https://doi.org/10.1108/JCM-03-2017-2141

  26. Drus, Z., & Khalid, H. (2019). Sentiment analysis in social media and its application: Systematic literature review. Procedia Computer Science, 161, 707–714. https://doi.org/10.1016/j.procs.2019.11.174

  27. Efijemue, O., Ejimofor, I., & Owolabi, O. S. (2023). Insider threat prevention in the US banking system. International Journal on Soft Computing, 14(3), 17–28. https://doi.org/10.5121/ijsc.2023.14302

  28. El Ayyubi, S., Anggraeni, L., & Mahiswari, A. D. (2018). Pengaruh bank syariah terhadap pertumbuhan ekonomi di Indonesia [The influence of Islamic banks on economic growth in Indonesia]. Al-Muzara’ah, 5(2), 88–106. https://doi.org/10.29244/jam.5.2.88-106

  29. Esmeli, R., Bader-El-Den, M., & Mohasseb, A. (2019). Context and short term user intention aware hybrid session based recommendation system. 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), 1–6. https://doi.org/10.1109/INISTA.2019.8778352

  30. Faza, F. T., Timur, Y. P., Mutmainah, L., & Rusgianto, S. (2022). You’ve over the line! Muslim consumers are resistant to opposite brand values. Shirkah: Journal of Economics and Business, 7(3), 219–238. https://doi.org/10.22515/shirkah.v7i3.529

  31. Geetha, M., & Kumari, J. A. (2012). Analysis of churn behavior of consumers in Indian telecom sector. Journal of Indian Business Research, 4(1), 24–35. https://doi.org/10.1108/17554191211206780

  32. Gupta, A., Saruparia, Dr. C., & Giri, Dr. A. K. (2023). Economic analysis of cyber risk for financial institutions. GNLU Journal of Law & Economics, 6(2), 8–32. https://doi.org/10.69893/gjle.2023.000058

  33. Hartmann, J., Heitmann, M., Siebert, C., & Schamp, C. (2023). More than a feeling: Accuracy and application of sentiment analysis. International Journal of Research in Marketing, 40(1), 75–87. https://doi.org/10.1016/j.ijresmar.2022.05.005

  34. Hasan, M. F., & Al-Ramadan, N. S. (2021). Cyber-attacks and cyber security readiness: Iraqi private banks case. Social Science and Humanities Journal  (SSHJ), 5(8), 2312–2323. https://sshjournal.com/index.php/sshj/article/view/739/

  35. Hasan, M. R., Abdunurova, A., Wang, W., Zheng, J., & Shams, S. M. R. (2021). Using deep learning to investigate digital behavior in culinary tourism. Journal of Place Management and Development, 14(1), 43–65. https://doi.org/10.1108/JPMD-03-2020-0022

  36. Heimerl, F., Lohmann, S., Lange, S., & Ertl, T. (2014). Word cloud explorer: Text analytics based on word clouds. 2014 47th Hawaii International Conference on System Sciences, 1833–1842. https://doi.org/10.1109/HICSS.2014.231

  37. Hosen, M. N., Lathifah, F., & Jie, F. (2021). Perception and expectation of customers in Islamic bank perspective. Journal of Islamic Marketing, 12(1), 1–19. https://doi.org/10.1108/JIMA-12-2018-0235

  38. Huang, D., Zhou, J., Mu, D., & Yang, F. (2014). Retweet behavior prediction in Twitter. 2014 Seventh International Symposium on Computational Intelligence and Design, 30–33. https://doi.org/10.1109/ISCID.2014.187

  39. Hudaefi, F. A., & Badeges, A. M. (2022). Maqāṣid al-Sharī‘ah on Islamic banking performance in Indonesia: A knowledge discovery via text mining. Journal of Islamic Marketing, 13(10), 2069–2089. https://doi.org/10.1108/JIMA-03-2020-0081

  40. Hudaefi, F. A., Zainal, M. H., Choirin, M., & Junari, U. L. (2021). Zakat in virtual world: Sentiment analysis of netizens’ opinion on Twitter (Puskas Working Paper Series (PWPS) 2020). Center of Strategic Studies (PUSKAS) BAZNAS. https://puskasbaznas.com/publications/published/pwps/1541-zakat-in-virtual-world-sentiment-analysis-of-netizens-opinion-on-twitter

  41. International Monetary Fund. (2020). Cyber Risk and Financial Stability: It’s a Small World After All Cyber Risk and Financial Stability: It’s a Small World After All Authorized for distribution This note has benefited from help and input from colleagues Yan Carriere-Swallow, Attila Csajbok;

  42. Jadhav, A., Jagtap, P., Gurav, S., Jadhav, S., Jadhav, N., & Akkalkot, A. (2023). A survey on text mining—Techniques, application. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(3), 338–343. https://doi.org/10.32628/CSEIT2390391

  43. Jeong, W., Kim, J., & Jeong, H. (2023). Information extraction from unstructured data on microplastics through text mining. Journal of Korean Society of Environmental Engineers, 45(1), 34–42. https://doi.org/10.4491/KSEE.2023.45.1.34

  44. Jin, Y. (2017). Development of word cloud generator software based on Python. Procedia Engineering, 174, 788–792. https://doi.org/10.1016/j.proeng.2017.01.223

  45. Kakar, S., Dhaka, D., & Mehrotra, M. (2021). Value-based retweet prediction on Twitter. Informatica, 45(2), 267–276. https://doi.org/10.31449/inf.v45i2.3465

  46. Kallam, Y. R., Panchumarthi, L. Y., Parchuri, L., Hajarathaiah, K., Enduri, M. K., & Anamalamudi, S. (2023). Advancements in sentiment analysis: A deep learning approach. 2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN), 206–210. https://doi.org/10.1109/CICN59264.2023.10402154

  47. Kartika, T., Firdaus, A., & Najib, M. (2019). Contrasting the drivers of customer loyalty; financing and depositor customer, single and dual customer, in Indonesian Islamic bank. Journal of Islamic Marketing, 11(4), 933–959. https://doi.org/10.1108/JIMA-04-2017-0040

  48. Kemp, S. (2021, February 11). Digital in Indonesia: All the statistics you need in 2021 [HTML]. DataReportal. https://datareportal.com/reports/digital-2021-indonesia

  49. Keramati, A., Ghaneei, H., & Mirmohammadi, S. M. (2016). Developing a prediction model for customer churn from electronic banking services using data mining. Financial Innovation, 2(1), 10. https://doi.org/10.1186/s40854-016-0029-6

  50. Khanday, A. M. U. D., Khan, Q. R., & Rabani, S. T. (2021). Identifying propaganda from online social networks during COVID-19 using machine learning techniques. International Journal of Information Technology, 13(1), 115–122. https://doi.org/10.1007/s41870-020-00550-5

  51. Khodabandehlou, S., & Rahman, M. Z. (2017). Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior. Journal of Systems and Information Technology, 19(1/2), 65–93. https://doi.org/10.1108/JSIT-10-2016-0061

  52. Kurniawan, M. A., Anwar, M., & Nidar, S. R. (2022). Developing a strategy for Islamic money market model to enhance quality of Islamic banking performance during the pandemic in Indonesia 2021. Quality - Access to Success, 23(190), 261–268. https://doi.org/10.47750/QAS/23.190.28

  53. Langat, B., & Atheru, G. (2023). Customer relationship management and competitive advantage of commercial banks in kenya. International Journal of Business Management, Entrepreneurship and Innovation, 5(4), 22–35. https://doi.org/10.35942/hjxgxf95

  54. Li, M., Ch’ng, E., Chong, A. Y. L., & See, S. (2018). Multi-class Twitter sentiment classification with emojis. Industrial Management & Data Systems, 118(9), 1804–1820. https://doi.org/10.1108/IMDS-12-2017-0582

  55. Lutfi, B. A., Prasetyo, A., Timur, Y. P., & Rifqi, M. (2023). Exploring gender differences in determinants of Bank Aladin Sharia adoption: A multi-group analysis approach. Jurnal Ekonomi Dan Bisnis Airlangga, 33(1), 40–52. https://doi.org/10.20473/jeba.V33I12023.40-52

  56. Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R., & Yang, Y. (2020). Global sentiments surrounding the COVID-19 pandemic on Twitter: Analysis of Twitter trends. JMIR Public Health and Surveillance, 6(2), e19447. https://doi.org/10.2196/19447

  57. Masvood, Y. (2019). A critical evaluation of articles related to Islamic banking. International Journal of Recent Technology and Engineering, 8(2S4), 302–306. https://doi.org/10.35940/ijrte.B1057.0782S419

  58. Mbama, C. I., & Ezepue, P. O. (2018). Digital banking, customer experience and bank financial performance: UK customers’ perceptions. International Journal of Bank Marketing, 36(2), 230–255. https://doi.org/10.1108/IJBM-11-2016-0181

  59. Minaryanti, A. A., & Mihajat, M. I. S. (2024). A systematic literature review on the role of Sharia governance in improving financial performance in Sharia banking. Journal of Islamic Accounting and Business Research, 15(4), 553–568. https://doi.org/10.1108/JIABR-08-2022-0192

  60. Mittal, R., Ahmed, W., Mittal, A., & Aggarwal, I. (2021). Twitter users’ coping behaviors during the COVID-19 lockdown: An analysis of tweets using mixed methods. Information Discovery and Delivery, 49(3), 193–202. https://doi.org/10.1108/IDD-08-2020-0102

  61. Mohd. Shariff, R. A., Bahrul Ilmi, M., & Mohamad, M. H. S. (2022). Linking corporate governance with organisational growth: Evidence from Indonesian Islamic banks. Journal of Islamic Accounting and Business Research, 13(4), 623–648. https://doi.org/10.1108/JIABR-05-2021-0153

  62. Muneer, A., Faizan Ali, R., Alghamdi, A., Mohd Taib, S., Almaghthawi, A., & Ghaleb, E. A. A. (2022). Predicting customers churning in banking industry: A machine learning approach. Indonesian Journal of Electrical Engineering and Computer Science, 26(1), 539–549. https://doi.org/10.11591/ijeecs.v26.i1.pp539-549

  63. Nabila, F., & Thamrin, H. (2022). Kontribusi perbankan syariah terhadap pertumbuhan ekonomi negara di Asia Tenggara [Contribution of Islamic banking to economic growth in Southeast Asian countries]. Jurnal Tabarru’: Islamic Banking and Finance, 5(2), 336–376. https://doi.org/10.25299/jtb.2022.vol5(2).10371

  64. Nasar, A., Kamarudin, S., Rizal, A. M., Ngoc, V. T. B., & Shoaib, S. M. (2019). Short-term and long-term entrepreneurial intention comparison between Pakistan and Vietnam. Sustainability, 11(23), 6529. https://doi.org/10.3390/su11236529

  65. Nemes, L., & Kiss, A. (2021). Social media sentiment analysis based on COVID-19. Journal of Information and Telecommunication, 5(1), 1–15. https://doi.org/10.1080/24751839.2020.1790793

  66. Nguyen, T. T., Chang, K., & Hui, S. C. (2011). Word cloud model for text categorization. 2011 IEEE 11th International Conference on Data Mining, 487–496. https://doi.org/10.1109/ICDM.2011.156

  67. Nurillah, S. L., Aini, Z. N., Timur, Y. P., & Widiastuti, T. (2022). Online review and rating on consumer purchase intention: The moderating role of religiosity. Jurnal Ekonomi Dan Bisnis Airlangga, 32(2), 160–175. https://doi.org/10.20473/jeba.V32I22022.160-175

  68. Otoritas Jasa Keuangan. (2021). Roadmap pengembangan perbankan Indonesia 2020—2025 [Roadmap of Indonesian banking development 2020—2025]. Otoritas Jasa Keuangan. https://ojk.go.id/id/berita-dan-kegiatan/info-terkini/Pages/-Roadmap-Pengembangan-Perbankan-Indonesia-2020---2025.aspx

  69. Otoritas Jasa Keuangan. (2023). Statistik perbankan syariah—Desember 2022 [Islamic banking statistics—December 2022]. Otoritas Jasa Keuangan. https://ojk.go.id/id/kanal/syariah/data-dan-statistik/statistik-perbankan-syariah/Pages/Statistik-Perbankan-Syariah---Desember-2022.aspx

  70. Pusparini, M. D., Fatimah, A., & Andriansyah, Y. (2020). User perception of Shari’ah compliance in PayTren. In F. L. Gaol, N. Filimonova, I. Frolova, & I. Vladimirovna (Eds.), Inclusive Development of Society: Proceedings of the 6th International Conference on Management and Technology in Knowledge, Service, Tourism & Hospitality (SERVE 2018) (pp. 256–263). CRC Press.

  71. Putri, C. S., Herianingrum, S., Ramadhanty, R. P., Zubaid, N. L., & Timur, Y. P. (2023). Relationship between Islamic bank consumptive financing and gross regional domestic product in Indonesia, 2016-2020. Journal of Islamic Economics Lariba, 9(1), 97–114. https://doi.org/10.20885/jielariba.vol9.iss1.art6

  72. Rakhmawati, R., & Rizky, A. W. (2023). The intention of university students to donate at zakat institution through digital payment. Journal of Islamic Economics Lariba, 9(1), 201–220. https://doi.org/10.20885/jielariba.vol9.iss1.art12

  73. Ratnasari, R. T., Timur, Y. P., Battour, M., & Jamilu, U. (2023). An effort to increase waqf intention: The role of celebrity endorsers in social campaigns. Al-Uqud : Journal of Islamic Economics, 7(2), 154–171. https://doi.org/10.26740/aluqud.v7n2.p154-171

  74. Riyadi, S. (2021). The effects of image, brand and quality on customer loyalty of Sharia banking. The Journal of Asian Finance, Economics and Business, 8(3), 1315–1325. https://doi.org/10.13106/JAFEB.2021.VOL8.NO3.1315

  75. Rozzani, N., Mohamed, I. S., & Syed Yusuf, S. N. (2016). Technology for Islamic microfinance’s disbursement and repayment system. International Journal of Social Economics, 43(12), 1271–1283. https://doi.org/10.1108/IJSE-05-2015-0115

  76. Safitri, I. K. (2023, May 19). Serba-serbi serangan Lockbit ke BSI [Lockbit’s attack on BSI] [HTML]. Tempo. https://grafis.tempo.co/read/3315/serba-serbi-serangan-lockbit-ke-bsi

  77. Sandhya N., Samuel, P., & Chacko, M. (2019). Feature intersection for agent-based customer churn prediction. Data Technologies and Applications, 53(3), 318–332. https://doi.org/10.1108/DTA-03-2019-0043

  78. Santika, E. F. (2023, May 16). Saham BSI langsung ambles setelah datanya bocor di dark web [BSI shares immediately plummet after data leaked on the dark web] [HTML]. Databoks. https://databoks.katadata.co.id/pasar/statistik/db792454fd3860c/saham-bsi-langsung-ambles-setelah-datanya-bocor-di-dark-web

  79. Sarmast, Z., Shokouhyar, S., Ghanadpour, S. H., & Shokoohyar, S. (2023). Unravelling the potential of social media data analysis to improve the warranty service operation. Industrial Management & Data Systems, 123(5), 1281–1309. https://doi.org/10.1108/IMDS-07-2022-0427

  80. Shaffiei, Z. A., Hamzah, A. S. S. S. A., Rashid, S. M. H., & Oshima, N. (2023). Role of text mining in extracting valuable information from text data. Journal of Advanced Research in Applied Sciences and Engineering Technology, 32(1), 263–271. https://doi.org/10.37934/araset.32.1.263271

  81. Shankar, S. P., Gudadinni, S. M., & Mohta, R. (2024). A comprehensive study of cyber threats in the banking industry. In S. Saeed, N. Azizi, S. Tahir, M. Ahmad, & A. M. Almuhaideb (Eds.), Advances in Business Information Systems and Analytics (pp. 244–269). IGI Global. https://doi.org/10.4018/979-8-3693-0839-4.ch011

  82. Simanjuntak, I., Sudiarti, S., & Yanti, N. (2023). The impact of implementing Aceh Qanun No. 11 of 2018 concerning Sharia Financial Institutions on the management of Sharia insurance institutions. Journal of Islamic Economics Lariba, 9(1), 239–254. https://doi.org/10.20885/jielariba.vol9.iss1.art14

  83. Sutrisno, S., & Widarjono, A. (2018). Maqasid Sharia Index, banking risk and performance cases in Indonesian Islamic banks. Asian Economic and Financial Review, 8(9), 1175–1184. https://doi.org/10.18488/journal.aefr.2018.89.1175.1184

  84. Svendsen, G. B., & Prebensen, N. K. (2013). The effect of brand on churn in the telecommunications sector. European Journal of Marketing, 47(8), 1177–1189. https://doi.org/10.1108/03090561311324273

  85. Tessem, B., Bjørnestad, S., Chen, W., & Nyre, L. (2015). Word cloud visualisation of locative information. Journal of Location Based Services, 9(4), 254–272. https://doi.org/10.1080/17489725.2015.1118566

  86. Timur, Y. P. (2022). Apakah digital cause-related marketing berpengaruh terhadap niat beli konsumen muslim pada produk UMKM makanan halal? [Does digital cause-related marketing influence Muslim consumers’ purchase intention for halal food MSME products?]. Prosiding National Seminar on Accounting, Finance, and Economics (NSAFE), 2, 1–16. http://conference.um.ac.id/index.php/nsafe/article/view/2430

  87. Timur, Y. P., & Herianingrum, S. (2022). The influence of entrepreneurship education on entrepreneurial intentions in Generation Z Muslim. Jurnal Ekonomi Dan Bisnis Airlangga, 32(1), 81–92. https://doi.org/10.20473/jeba.V32I12022.81-92

  88. Timur, Y. P., Ratnasari, R. T., & Author, N. (2022). Celebrity endorsers vs expert endorsers: Who can affect consumer purchase intention for halal fashion product? Jurnal Ekonomi Dan Bisnis Islam (Journal of Islamic Economics and Business), 8(2), 220–236. https://doi.org/10.20473/jebis.v8i2.37529

  89. Timur, Y. P., Ratnasari, R. T., Hadi, T. S., & Sari, D. P. (2023). What do Indonesian netizens think about the emoney?: A sentiment analysis with machine learning. Jurnal Riset Akuntansi Dan Bisnis Airlangga, 8(1), 1452–1469. https://doi.org/10.20473/jraba.v8i1.44940

  90. Timur, Y. P., Ratnasari, R. T., Pitchay, A. A., & Jamilu, U. (2023a). Public perception of amil zakat institutions in Indonesia: Insight discovery from machine learning. Jurnal Ekonomi Dan Bisnis Islam (Journal of Islamic Economics and Business), 9(2), 373–400. https://doi.org/10.20473/jebis.v9i2.45416

  91. Timur, Y. P., Ratnasari, R. T., Pitchay, A. A., & Jamilu, U. (2023b). What do Indonesian think about waqf? A sentiment analysis using machine learning. Ziswaf: Jurnal Zakat Dan Wakaf, 10(1), 98. https://doi.org/10.21043/ziswaf.v10i1.20224

  92. Usman, H., Projo, N. W. K., Chairy, C., & Haque, M. G. (2022). The exploration role of Sharia compliance in technology acceptance model for e-banking (Case: Islamic bank in Indonesia). Journal of Islamic Marketing, 13(5), 1089–1110. https://doi.org/10.1108/JIMA-08-2020-0230

  93. Wang, Y., & Li, B. (2015). Sentiment analysis for social media images. 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 1584–1591. https://doi.org/10.1109/ICDMW.2015.142

  94. Widarjono, A., & Misanam, M. (2023). Determinant of Murabaha financing in Indonesian Sharia banking: The ARDL and NARDL approach. Journal of Islamic Economics Lariba, 9(2), 395–416. https://doi.org/10.20885/jielariba.vol9.iss2.art7

  95. Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2), 617–663. https://doi.org/10.1007/s10115-018-1236-4

  96. Yusuf, M., Sumarno, S., & Komarudin, P. (2022). Bank digital syariah di Indonesia: Telaah regulasi dan perlindungan nasabah [Islamic digital banks in Indonesia: A review of regulations and customer protection]. Al-Infaq: Jurnal Ekonomi Islam, 13(2), 271. https://doi.org/10.32507/ajei.v13i2.1654

  97. Zahoor, Z., Ud-din, M., & Sunami, K. (2016). Challenges in privacy and security in banking sector and related countermeasures. International Journal of Computer Applications, 144(3), 24–35. https://ijcaonline.org/archives/volume144/number3/25161-2016910173/

  98. Zeitun, R., & Benjelloun, H. (2012). The efficiency of banks and the financial crisis in a developing economy: The case of Jordan. International Review of Accounting, Banking and Finance, 4(2), 28–60. http://www.irabf.org/upload/journal/prog/2012v4n2_2.pdf

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