Citation

Gnoh, Hao Qian and Keoy, Kay Hooi and Iqbal, Javid and Anjum, Shaik Shabana and Yeo, Sook Fern and Lim, Ai Fern and Lim, Wei Lee and Chaw, Lee Yen (2024) Enhancing Business Sustainability Through Technology-Enabled AI: Forecasting Student Data and Comparing Prediction Models for Higher Education Institutions (HEIs). PaperASIA, 40 (2b). pp. 48-58. ISSN 0218-4540

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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.

Item Type:	Article
Uncontrolled Keywords:	Business analytics, Decision making, Business sustainability, Forecasting, Quality education
Subjects:	H Social Sciences > HF Commerce > HF5001-6182 Business L Education > LB Theory and practice of education > LB2300 Higher Education
Divisions:	Faculty of Business (FOB)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Aug 2024 02:14
Last Modified:	01 Aug 2024 02:14
URII:	http://shdl.mmu.edu.my/id/eprint/12692

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