Job Standard Parameters from Online Job Vacancy

Herlambang Haryo Putro, Nur Rakhmawati Rakhmawati

Abstract


The internet has provided an efficient and economical way to provide job vacancy information to applicants in a way that is far more dynamic and consistent than happened in the past. This is directly proportional to the number of companies exploiting online technology (job portals, company websites, etc.) to make job advertisements reach a growing audience. To further optimize the selection process concerning processing time and accuracy, researchers have begun developing a sophisticated search engine to automatically sort resumes based on job offer requirements. Overcoming this problem requires the Information Extraction (IE) process. Research on IE regarding vacancies actually already exists and has been applied. Research on IE regarding vacancies actually already exists and has been applied In addition, we want to develop previous research involving all job vacancies websites to a wider extent. We realize that this limitation makes the extraction process difficult. we try to define job problems more easily, such as identifying various JSPs on job vacancy websites in Indonesia as a first step in the extraction process. Our findings in the survey of 14 job vacancy websites are 26 job standard parameters including structure collection and extraction methods. This study provides a detailed description of each component of information extraction on the job vacancy website in Indonesia. Starting from identifying the type of Structured Extraction to the output of extraction. This study also developed JSP from previous research.

Keywords


Job Standard Parameters; Entity; Method Extraction.

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References


G. V. A. N. Hoye and F. Lievens, “Investigating web-based recruitment sources: Employee testimonials vs word-of-mouse,” Int. J. Sel. Assess., vol. 15, no. 4, pp. 372–382, 2007, doi: https://doi.org/10.1111/j.1468-2389.2007.00396.x.

P. Montuschi, V. Gatteschi, F. Lamberti, A. Sanna, and C. Demartini, “Job recruitment and job seeking processes: how technology can help,” IT Prof., vol. 16, no. 5, pp. 41–49, 2014, doi: 10.1109/MITP.2013.62.

M. F. Koh and Y. C. Chew, “Intelligent job matching with self-learning recommendation engine,” Procedia Manuf., vol. 3, pp. 1959–1965, 2015, doi: 10.1016/j.promfg.2015.07.241.

K. Adnan and R. Akbar, “Limitations of information extraction methods and techniques for heterogeneous unstructured big data,” Int. J. Eng. Bus. Manag., vol. 11, pp. 1–23, 2019, doi: 10.1177/1847979019890771.

D. Celik et al., “Towards an Information Extraction System Based on Ontology to Match Resumes and Jobs,” in Proceedings - International Computer Software and Applications Conference, 2013, pp. 333–338, doi: 10.1109/COMPSACW.2013.60.

S. Sarawagi, “Information extraction,” trends r databases, vol. 1, no. 3, pp. 261–377, 2007, doi: 10.1561/1500000003.

J. Zhang and W. Z. Ding, “An Improved Ontology-Based Web Information Extraction,” in Proceedings - 2015 International Conference of Educational Innovation Through Technology, EITT 2015, 2016, pp. 37–41, doi: 10.1109/EITT.2015.14.

F. Gutierrez, D. Dou, S. Fickas, D. Wimalasuriya, and H. Zong, “A hybrid ontology-based information extraction system,” J. Intell. Mater. Syst. Struct., vol. 42, no. 6, pp. 798–820, 2016, doi: 10.1177/1045389X14554132.




DOI: http://dx.doi.org/10.12962/j23546026.y2020i6.8905

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