Muhammad Faisal

Work place: Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, 90221, Indonesia

E-mail: muhfaisal@unismuh.ac.id

Website: https://orcid.org/0000-0003-1469-9468

Research Interests:

Biography

Muhammad Faisal his Bachelor of Information Systems (S.SI) in Information Systems Department from STMIK Profesional, Makassar, Indonesia, in 2011, and his Master of Computer Science (M.Kom) in Informatics Engineering Department from Universitas Hasanuddin, Gowa, Indonesia, in 2014. He holds Ph.D degree from the School of Science and Technology, Doctoral Programme, Asia E University Malaysia. He is currently a lecturer and researcher at the Department of Informatics, Universitas Muhammadiyah Makassar, Indonesia. His research interests include Artificial intelligence, Data Mining, Decision Support Systems, Image Processing, Deep Learning, and Machine Learning.

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

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

DOI: https://doi.org/10.5815/ijmecs.2025.04.01, Pub. Date: 8 Aug. 2025

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.

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