Imam Asrowardi

Work place: Department of Internet Engineering Technology, Politeknik Negeri Lampung, Lampung, 35144, Indonesia

E-mail: imam@polinela.ac.id

Website: https://orcid.org/0000-0002-0115-0874

Research Interests:

Biography

Imam Asrowardi has been a lecturer in the Internet Engineering Technology Study Program at Politeknik Negeri Lampung since 2015 and currently holds the position of Associate Professor. He is actively involved in various national professional associations (IAII, APTIKOM, Informatics Engineering Division of the Indonesian Engineers Association) and international organizations (IAENG), both as a member and an administrator. Imam holds numerous industry certifications in various fields, including MTCINE, MTCRE, MTCWE, MTCUME, MTCIPv6E, MTA (Networking), CCC-BDF, Cisco Academy Trainer (CCNA, CyberOps Associate, DevNet Associate, CCNP), MikroTik Academy Trainer, Programmer (BNSP), Competency Assessor (BNSP), National Procurement Expert (BNSP), and Cisco Certified Support Technician Networking (CCST Networking).

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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