Enhancing Business Sustainability Through Technology-Enabled AI: Forecasting Student Data and Comparing Prediction Models for Higher Education Institutions (HEIs)

Authors

  • Hao Qian Gnoh Institute of Computer Science and Digital Innovation, UCSI University, 56000 Cheras, Kuala Lumpur, Malaysia
  • Kay Hooi Keoy UCSI Graduate Business School, UCSI University, 56000 Cheras, Kuala Lumpur, Malaysia
  • Javid Iqbal Institute of Computer Science and Digital Innovation, UCSI University, 56000 Cheras, Kuala Lumpur, Malaysia
  • Shaik Shabana Anjum School of Computer Science, Faculty of Innovation and Technology, Taylor’s University, 47500 Subang Jaya, Selangor, Malaysia
  • Sook Fern Yeo Faculty of Business, Multimedia University, 75450 Bukit Beruang, Melaka, Malaysia
  • Ai-Fern Lim UCSI Graduate Business School, UCSI University, 56000 Cheras, Kuala Lumpur, Malaysia
  • WeiLee Lim UCSI Graduate Business School, UCSI University, 56000 Cheras, Kuala Lumpur, Malaysia
  • Lee Yen Chaw UCSI Graduate Business School, UCSI University, 56000 Cheras, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.59953/paperasia.v40i2b.86

Keywords:

Business analytics, Decision making, Business sustainability, Forecasting, Quality education

Abstract

This study aims to enhance business sustainability in the context of Higher Education Institutions (HEIs) by utilizing AI and forecasting techniques. It explores the development and comparison of prediction models, including the use of dashboard development, to support decision-making processes within HEIs. The study covers various aspects, including the background of forecasting and prediction models, the use of specific models such as the Prophet Model, Long Short-Term Memory (LSTM) Model, and Polynomial Regression Model, as well as the importance of dashboards for HEIs. The methodology section outlines the data collection and preparation process, model selection, approach, diagrams, functional and non-functional requirements, justification of tools, and libraries and models used. The implementation section delves into the system design and development of the dashboard, including the login page, homepage, forecast page, and insert data page. As for the findings, the LSTM Model has proven to be the most accurate and suitable model to be implemented for forecasting student enrolment data in this study. The dashboard's future enhancements involve adding more faculties, predictive features for resource allocation, refining the visual identity, improving user registration on the login page, and exploring better models for student enrolment predictions. Overall, the study provides valuable insights into the application of AI and forecasting techniques in HEIs, aiming to enhance business sustainability and decision-making processes. It contributes to the growing body of knowledge on the use of technology-enabled AI in higher education institutions, with a focus on forecasting student enrolment data and developing prediction models.

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Published

2024-04-02

How to Cite

Gnoh, H. Q., Keoy, K. H., Iqbal, J., Shaik Shabana Anjum, Yeo, S. F., Lim, A.-F., Lim, W., & Chaw, L. Y. (2024). Enhancing Business Sustainability Through Technology-Enabled AI: Forecasting Student Data and Comparing Prediction Models for Higher Education Institutions (HEIs). PaperASIA, 40(2b), 48–58. https://doi.org/10.59953/paperasia.v40i2b.86