Data analytic for business based prediction

Mohamed Yusoff, Syarifah Adilah and Johan, Elly Johana and Warris, Saiful Nizam and Othman, Jamal (2026) Data analytic for business based prediction. Merging Lanes: Where E-Learning Diversity Meets Future Trends, 11. pp. 162-171. ISSN 978-629-98755-9-8

Official URL: https://appspenang.uitm.edu.my/sigcs/

Abstract

Currently, business processes are perceived not merely as a sequence of activities responding to an event to generate output, but as a complex system involving the interplay of individuals, technologies, strategies, and business rules to attain certain business outcomes. Consequently, the analysis of a substantial volume of data is essential not only for present operations and several years ahead but also for future trends and long-term objectives. This study aims to present the concept of data analytics within the business domain, integrating it with a business framework specifically for operational purposes, and incorporating machine learning for predictive analytics, culminating in the evaluation of classification predictions. Information is a crucial asset that enables future business planning through a data-driven methodology and demonstrates the importance of business analytics for future success.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Mohamed Yusoff, Syarifah Adilah
syarifah.adilah@uitm.edu.my
Johan, Elly Johana
ellyjohana@uitm.edu.my
Warris, Saiful Nizam
saifulwar@uitm.edu.my
Othman, Jamal
jamalothman@usm.edu.my
Contributors:
Contribution
Name
Email / ID Num.
Advisor
Abd Rahman, Nor Hanim
UNSPECIFIED
Chief Editor
Othman, Jamal
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Analysis > Analytical methods used in the solution of physical problems
Divisions: Universiti Teknologi MARA, Pulau Pinang > Permatang Pauh Campus
Journal or Publication Title: Merging Lanes: Where E-Learning Diversity Meets Future Trends
ISSN: 978-629-98755-9-8
Volume: 11
Page Range: pp. 162-171
Keywords: Business process, Data analytics, Machine learning
Date: April 2026
URI: https://ir.uitm.edu.my/id/eprint/139361
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139361

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