Integration of Artificial Intelligence and Data Mining in Accounting Information Systems for Analysis of New Student Uptake Patterns: An Open University Case Study
DOI:
https://doi.org/10.36555/jasa.v10i1.2992Keywords:
Artificial Intelligence, Accounting Information Systems, Data MiningAbstract
Increased enrollment of new students is an important measure of the university's ability to meet the needs of Education. To remain relevant and competitive, institutions are increasingly expected to incorporate artificial intelligence (AI) technologies that improve data-driven decision-making. The combination of data mining methods and Accounting Information Systems, which utilize computational techniques to analyze vast data sets can reveal hidden patterns. This study investigates the application of artificial intelligence in creating a direct data mining tool that aims to identify trends in new student enrollment at the Open University. Using a research and development approach, the study examined admissions data from 2022 to 2024 to uncover underlying patterns and significant factors influencing enrollment dynamics. The results show that AI-integrated applications significantly improve the efficiency of data analysis while improving the reliability and usability of institutional data for strategic purposes. The study contributes to a larger conversation about how aligning AI implementation with institutional goals can drive innovation, accountability, and responsiveness in the context of open and distance learning. In summary, the findings imply that incorporating AI into data management and development processes provides greater clarity regarding future trends, making AI a powerful forecasting and decision-making resource for higher education institutions.
References
Alvi, M. (2016). A manual for selecting sampling techniques in research. https://mpra.ub.uni-muenchen.de/id/eprint/70218
Bustami, B. (2013) Penerapan Algoritma Naive Bayes Untuk Mengklasifikasi Data Nasabah Asuransi, TECHSI : Jurnal Penelitian Teknik Informatika, Vol. 3, No.2, Hal. 127-146. https://doi.org/10.29103/techsi.v5i2.154
Gitman, L. J., & Zutter, C. J. (2012). Principles of Managerial Finance. Pearson
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Han, J., Kamber, M., Berzal, F., & Marín, N. (2002). Data Mining: Concepts and Techniques. SIGMOD Record, 31(2), 66–68. https://doi.org/10.1145/565117.565130
Han, J., Kamber, M., kaufmann, D. (2006). Concepts and techniques. Liacs.Leidenuniv.Nl.
Jantawan, B., & Tsai, C. F. (2014). A comparison of filter and wrapper approaches with data mining techniques for categorical variables selection. International Journal of Innovative Research in Computer and Communication Engineering, 2(6), 4501-4508.
Kotler, P. (2012). Kotler on marketing.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Patil, T. R., & Sherekar, M. S., (2013). Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification, International Journal of Computer Science and Applications, Vol. 6, No. 2, Hal 256- 261.
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Prentice Hall
Santosa, B., Conway, T., & Trafalis, T. (2007). A hybrid knowledge based-clustering multi-class svm approach for genes expression analysis. Springer Optimization and Its Applications, 7, 231–274. https://doi.org/10.1007/978-0-387-69319-4_15
Singh, S. (2013). Performance analysis of engineering students for recruitment using classification data mining techniques. Ijcset.Net. Retrieved November 29, 2023, from http://ijcset.net/docs/Volumes/volume3issue2/ijcset2013030202.pdf
Sivaram, N., & Ramar, K. (2010). Applicability of clustering and classification algorithms for recruitment data mining. International Journal of Computer Applications, 4(5), 23-28. https://ui.adsabs.harvard.edu/abs/2010IJCA....4e..23S/abstract
Witten, D. M. (2011). Classification and clustering of sequencing data using a Poisson model. The Annals of Applied Statistics, 5(4), 2493–2518. https://doi.org/10.1214/11-AOAS493
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?. International journal of educational technology in higher education, 16(1), 39. https://link.springer.com/content/pdf/10.1186/s41239-019-0171-0%E2%80%8C.pdf
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