Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis
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 fie...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10237219/ |
| _version_ | 1797688463199305728 |
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| author | Puji Catur Siswipraptini Harco Leslie Hendric Spits Warnars Arief Ramadhan Widodo Budiharto |
| author_facet | Puji Catur Siswipraptini Harco Leslie Hendric Spits Warnars Arief Ramadhan Widodo Budiharto |
| author_sort | Puji Catur Siswipraptini |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-12T01:32:20Z |
| format | Article |
| id | doaj.art-7ea9e75e5052445c996c48308e799cdf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T01:32:20Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7ea9e75e5052445c996c48308e799cdf2023-09-11T23:02:46ZengIEEEIEEE Access2169-35362023-01-0111946479466310.1109/ACCESS.2023.331120310237219Information Technology Job Profile Using Average-Linkage Hierarchical Clustering AnalysisPuji Catur Siswipraptini0https://orcid.org/0009-0008-7833-5552Harco Leslie Hendric Spits Warnars1Arief Ramadhan2https://orcid.org/0000-0001-5501-7457Widodo Budiharto3https://orcid.org/0000-0003-2681-0901Computer Science Department, BINUS Graduate Program-Doctor of Computer Science, Bina Nusantara University, Jakarta, IndonesiaComputer Science Department, BINUS Graduate Program-Doctor of Computer Science, Bina Nusantara University, Jakarta, IndonesiaSchool of Computing, Telkom University, Bandung, IndonesiaComputer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, IndonesiaThe 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).https://ieeexplore.ieee.org/document/10237219/Information technology job profileskillsaverage-linkage hierarchical clustering analysismost frequent wordmost frequent phrase |
| spellingShingle | Puji Catur Siswipraptini Harco Leslie Hendric Spits Warnars Arief Ramadhan Widodo Budiharto Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis IEEE Access Information technology job profile skills average-linkage hierarchical clustering analysis most frequent word most frequent phrase |
| title | Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis |
| title_full | Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis |
| title_fullStr | Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis |
| title_full_unstemmed | Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis |
| title_short | Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis |
| title_sort | information technology job profile using average linkage hierarchical clustering analysis |
| topic | Information technology job profile skills average-linkage hierarchical clustering analysis most frequent word most frequent phrase |
| url | https://ieeexplore.ieee.org/document/10237219/ |
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