Nizirwan Anwar

Work place: Department of Informatics Engineering, Universitas Esa Unggul, Jakarta, 11510, Indonesia

E-mail: nizirwan.anwar@esaunggul.ac.id

Website: https://orcid.org/0000-0003-1189-9093

Research Interests:

Biography

Nizirwan Anwar is a Lecturer in the Informatics Engineering Study Program, Faculty of Computer Science, Esa Unggul University under the auspices of the Kemala Bangsa Education Foundation (YPKB). The author was born in the city of Bandung on July 24 1964, completed his bachelor’s degree from the Physics Study Program, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Bandung in 1989 and continued the Electrical Engineering Study Program, Faculty of Engineering (dh. Postgraduate Study Program) University of Indonesia, Jakarta, completing his studies in 1995, obtaining a Bachelor of Engineering Intermediate Professional (IPM) in 2022 and ASEAN Engineer (ASEAN.Eng) in 2023.

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 curriculum to ensure alignment with dynamic labour 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 curriculum 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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