Tracking employment trends in Malaysia using text mining technique
The Covid-19 pandemic has changed the world we live in today. In particular, Movement Control Orders (MCOs) that have been deployed nationwide also have an indirect impact on the job creation. With the large number of graduates who have graduated and those who do not have a job will make it even mor...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2021
|
| Online Access: | http://journalarticle.ukm.my/17840/1/jqma-17-1-paper14.pdf |
| _version_ | 1796932726625927168 |
|---|---|
| author | Syerina Azlin Md Nasir, Wan Fairos Wan Yaacob, |
| author_facet | Syerina Azlin Md Nasir, Wan Fairos Wan Yaacob, |
| author_sort | Syerina Azlin Md Nasir, |
| collection | UKM |
| description | The Covid-19 pandemic has changed the world we live in today. In particular, Movement Control Orders (MCOs) that have been deployed nationwide also have an indirect impact on the job creation. With the large number of graduates who have graduated and those who do not have a job will make it even more difficult to get a job. This study attempts to investigate the employment trends during the pandemic in Malaysia by extracting job advertisements randomly from JobStreet website from September to October 2020. A sample of 1050 documents was analysed using text mining technique on two driving factors, job title and location. The results reveal that the highest number of positions offered are managers and the place that offered the most jobs was in Kuala Lumpur followed by Selangor. Further analysis is performed using K-Mediods Clustering to cluster the job titles against the location to illustrate the employment trends in Malaysia, which resulted in similar outcomes. |
| first_indexed | 2024-03-06T04:36:51Z |
| format | Article |
| id | ukm.eprints-17840 |
| institution | Universiti Kebangsaan Malaysia |
| language | English |
| last_indexed | 2024-03-06T04:36:51Z |
| publishDate | 2021 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | dspace |
| spelling | ukm.eprints-178402022-01-07T00:40:44Z http://journalarticle.ukm.my/17840/ Tracking employment trends in Malaysia using text mining technique Syerina Azlin Md Nasir, Wan Fairos Wan Yaacob, The Covid-19 pandemic has changed the world we live in today. In particular, Movement Control Orders (MCOs) that have been deployed nationwide also have an indirect impact on the job creation. With the large number of graduates who have graduated and those who do not have a job will make it even more difficult to get a job. This study attempts to investigate the employment trends during the pandemic in Malaysia by extracting job advertisements randomly from JobStreet website from September to October 2020. A sample of 1050 documents was analysed using text mining technique on two driving factors, job title and location. The results reveal that the highest number of positions offered are managers and the place that offered the most jobs was in Kuala Lumpur followed by Selangor. Further analysis is performed using K-Mediods Clustering to cluster the job titles against the location to illustrate the employment trends in Malaysia, which resulted in similar outcomes. Penerbit Universiti Kebangsaan Malaysia 2021 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17840/1/jqma-17-1-paper14.pdf Syerina Azlin Md Nasir, and Wan Fairos Wan Yaacob, (2021) Tracking employment trends in Malaysia using text mining technique. Journal of Quality Measurement and Analysis, 17 (1). pp. 177-187. ISSN 1823-5670 https://www.ukm.my/jqma/jqma17-1/ |
| spellingShingle | Syerina Azlin Md Nasir, Wan Fairos Wan Yaacob, Tracking employment trends in Malaysia using text mining technique |
| title | Tracking employment trends in Malaysia using text mining technique |
| title_full | Tracking employment trends in Malaysia using text mining technique |
| title_fullStr | Tracking employment trends in Malaysia using text mining technique |
| title_full_unstemmed | Tracking employment trends in Malaysia using text mining technique |
| title_short | Tracking employment trends in Malaysia using text mining technique |
| title_sort | tracking employment trends in malaysia using text mining technique |
| url | http://journalarticle.ukm.my/17840/1/jqma-17-1-paper14.pdf |
| work_keys_str_mv | AT syerinaazlinmdnasir trackingemploymenttrendsinmalaysiausingtextminingtechnique AT wanfairoswanyaacob trackingemploymenttrendsinmalaysiausingtextminingtechnique |