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Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 301 Documents
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Automatic Skill Extraction across Job Postings, Course Learning Outcomes, and Skills Framework for the Information Age for Pre-Competency Mapping Process Badrus Zaman; Windarto Windarto; Rimuljo Hendradi
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 3 (2026): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.12.3.744-759

Abstract

Background: The increasing demand for Information Technology (IT) professionals is often not aligned with the unstructured representation of competencies across various document sources, including industry documents such as job postings and educational documents such as Course Learning Outcomes (CLOs) and the Skills Framework for the Information Age (SFIA). Differences in linguistic characteristics among these sources create challenges for automatic skill extraction. Objective: This study aims to compare the performance of four Natural Language Processing (NLP)-based skill extraction methods, namely skill-specific NER (SkillNER), Rapid Automatic Keyword Extraction (RAKE), Yet Another Keyword Extractor (YAKE!), and KeyBERT, across three domains namely job postings, CLOs, and SFIA Framework. Methods: This study used 60 documents consisting of 20 job postings, 20 CLOs, and 20 SFIA Framework. The methods used consist of ground truth annotation using Cohen's Kappa, document extraction, skill extraction and evaluation using precision, recall, and F1-score. Results: SkillNER consistently outperformed all comparison methods across all data sources, achieving F1-Scores of 0.613 for job postings, 0.896 for SFIA, and 0.818 for CLOs. RAKE and YAKE! produced F1-Scores ranging from 0.101 to 0.263, while KeyBERT demonstrated the lowest performance. Conclusion: NER-based approaches trained on skill taxonomies are more effective than general keyword extraction methods. Future studies should integrate query expansion mechanisms, apply transformer model fine-tuning on skill-domain datasets, and expand data coverage to improve result generalizability. Keywords: Course Learning Outcomes, SkillNER, Job Posting, Skill Extraction, Skill Framework for the Information Age

Page 31 of 31 | Total Record : 301

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