Muhammad Faisal; Titik Khawa Abd Rahman; Darniati Zainal; Husni Mubarak; Fadly Shabir; Nizirwan Anwar; Imam Asrowardi

Utilizing Machine Learning-Based Decision-Making to Align Higher Education Curriculum with Industry Requirements

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Author(s)

Muhammad Faisal ^1,* Titik Khawa Abd Rahman ² Darniati Zainal ¹ Husni Mubarak ³ Fadly Shabir ⁴ Nizirwan Anwar ⁵ Imam Asrowardi ⁶

1. Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, 90221, Indonesia

2. School of Science and Technology, Asia e University, Selangor, 47500, Malaysia

3. Department of Digital Business, Universitas Negeri Makassar, Makassar, 90221, Indonesia

4. Department of Design, State Polytechnic of Creative Media, Makassar, Indonesia

5. Department of Informatics Engineering, Universitas Esa Unggul, Jakarta, 11510, Indonesia

6. Department of Internet Engineering Technology, Politeknik Negeri Lampung, Lampung, 35144, Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2025.04.01

Received: 11 Feb. 2025 / Revised: 2 May 2025 / Accepted: 28 May 2025 / Published: 8 Aug. 2025

Index Terms

Curriculum evaluation, machine learning, Lq-ROFNs, MCDM, SVM, SSO, higher education planning

Abstract

The accelerating pace of industrial transformation necessitates a strategic reconfiguration of higher education curricula to ensure alignment with dynamic labor market demands. This study introduces a hybrid decision-making framework that integrates Machine Learning with Multi-Criteria Decision Making techniques to evaluate and classify the readiness and relevance of academic programs. The methodological core includes the Step-wise Weight Assessment Ratio Analysis, Linguistic q-Rung Orthopair Fuzzy Numbers, and the Multi-Attributive Border Approximation Area Comparison method for criteria weighting, coupled with a classification model based on Support Vector Machine optimized using the Salp Swarm Optimization algorithm. The results demonstrate the framework's efficacy in identifying curricular gaps and recommending adaptive enhancements, especially for programs categorized as “Needs Improvement.” Beyond classification, the system facilitates strategic curriculum planning, fosters pedagogical innovation, and promotes industry-responsive learning pathways. This study highlights the transformative potential of machine learning in higher education, equipping students with the skills required to navigate an increasingly dynamic professional landscape, while offering actionable insights into instructional redesign, competency-based delivery, and industry-informed pedagogy. Future research will explore longitudinal impact assessment and broader stakeholder integration to enhance the framework’s scalability and contextual adaptability.

Cite This Paper

Muhammad Faisal, Titik Khawa Abd Rahman, Darniati Zainal, Husni Mubarak, Fadly Shabir, Nizirwan Anwar, Imam Asrowardi, "Utilizing Machine Learning-Based Decision-Making to Align Higher Education Curriculum with Industry Requirements", International Journal of Modern Education and Computer Science(IJMECS), Vol.17, No.4, pp. 1-25, 2025. DOI:10.5815/ijmecs.2025.04.01

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