Earticle

초록

영어

Often, a worker’s occupational domain and educational qualifications mismatch, leading to a discrepancy with their actual skill set. This can lead to either underperformance or overperformance, as the competencies required by the company may differ from those possessed by potential employees. This research aims to develop a system for matching user profiles and job vacancies called the Job Matching System. The system can help individuals find jobs that suit their educational background and skills. By collecting large amounts of data from the Jobstreet.co.id website, the system automatically identifies job opportunities in four job categories, which are data analyst, digital marketing, developer, and machine learning. We used the Latent Dirichlet Allocation approach to analyze the gathered data and identify potential topics within large data sets. Based on this information, the database correlates educational background with appropriate job classifications. The "Job Matching" website has a built-in decision-making tool. Prior knowledge of the worker’s background is required to aid in the matching procedure and facilitate the alignment of competencies with the worker’s preferred industry throughout the matching process.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review
2.1. Big Data in Human Resource Management
2.2. Text Mining (TM) and Latent Dirichlet Allocation (LDA)
Ⅲ. Methodology
3.1. Data Collection: Web Scrapping
3.2. Data Preprocessing
3.3. Data Processing
3.4. Decision System Design
Ⅳ. Findings and Disc
4.1. Data Collection
4.2. Data Preprocessing and Processing
4.3. Decision System Design
4.4. Discussion
Ⅴ. Conclusion and Future Research Directions
Acknowledgement

키워드

저자

• Ronald Sukwadi [ Professor, Industrial Engineering and Professional Engineer Program, Atma Jaya Catholic University of Indonesia, Indonesia ]
• Vivi Triyanti [ Associate Professor, Industrial Engineering, Atma Jaya Catholic University of Indonesia, Indonesia ]
• Alexander Arya Sangkara [ Alumnus of Bachelor of Industrial Engineering, Atma Jaya Catholic University of Indonesia, Indonesia ]
• Arum Park [ Assistant Professor, Department of AI Service Marketing, Seoul Cyber University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658