Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis

Siswipraptini, Puji Catur and Warnars, Harco Leslie Hendric Spits and Ramadhan, Arief and Budiharto, Widodo (2023) Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis. IEEE Access, 11. pp. 94647-94663. ISSN 2169-3536

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Abstract

The growth in Information Technology (IT) jobs is predicted to reach 15 percent between 2021 and 2031. The growth of IT jobs has resulted in a remarkable change in all infrastructure, such as information, skills, and domains covered in IT job profiles. Unfortunately, job roles and skills in this field remain undefined. The gap between the supply and demand needs in the IT workforce must be filled immediately with an appropriate strategy. To fulfill industry needs, an in-depth analysis of IT job profiles is important. Therefore, it is important for educational programs to identify the competencies needed by the industry to update their output. This study aims to identify the job profiles required for IT job specialists by analyzing real-world job posts published online to identify hidden meanings from a textual database. A systematic semantic methodology was proposed using an average-linkage hierarchical clustering analysis. It resembles a tree structure technique to discover relevant phrases, relationships, and hidden meanings through semantic analysis. Occurrences of the most frequent words and phrases were extracted to reveal the domain knowledge of each IT job cluster. The result is a systematic semantic analysis of the IT job profile comprising the programming language, specialized type, duty, database, tools, and frameworks. The justification for each job profile was validated by 10 IT professionals from various private and government companies in Indonesia through Focus Group Discussions (FGD).

Item Type: Article
Uncontrolled Keywords: Information technology job profile, skills, average-linkage hierarchical clustering analysis ,most frequent word, most frequent phrase
Subjects: Bidang Keilmuan > Analisis Spasial
Bidang Keilmuan > Assessment
Bidang Keilmuan > Data Mining
Bidang Keilmuan > Data Science
Bidang Keilmuan > Database
Bidang Keilmuan > Deep learning
Jurnal
Divisions: Fakultas Telematika Energi > S1 Teknik Informatika
Depositing User: Yudha Formanto
Date Deposited: 05 Feb 2026 02:24
Last Modified: 05 Feb 2026 02:24
URI: https://repository.itpln.ac.id/id/eprint/4931

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