• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.100-103
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• 2021

Most companies study why employees resign their jobs to prevent the outflow of excellent human resources. To obtain the data needed for the study, employees are interviewed or surveyed before resignation. However, it is difficult to get accurate results because employees do not want to express their opinions that may be disadvantageous to working in a survey. Meanwhile, according to the data released by the Korea Labor Institute, the greater the difference between the minimum level of education required by companies and the level of employees' academic background, the greater the tendency to resign jobs. Therefore, based on these data, in this study, we would like to predict whether employees will leave the company based on data such as major, education level and company type. We generate four kinds of resignation prediction models using Decision Tree, XGBoost, kNN and SVM, and compared their respective performance. As a result, we could identify various factors that were not covered in previous study. It is expected that the resignation prediction model help companies recognize employees who intend to leave the company in advance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.369-375
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• 2016

Most companies are willing to spend money on security systems such as DRM, Mail filtering, DLP, USB blocking, etc., for data leakage prevention. However, in many cases, it is difficult that legal team take action for data case because usually the company recognized that after the employee had left. Therefore perceiving one's resignation before the action and building up adequate response process are very important. Throughout analyzing DRM log which records every single file's changes related with user's behavior, the company can predict one's resignation and prevent data leakage before those happen. This study suggests how to prevent for the damage from leaked confidential information throughout building the DRM monitoring process which can predict employee's resignation.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.297-300
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• 2021

대부분의 기업에서는 우수한 인적 자원의 유출을 방지하기 위해 직원들이 이직 및 퇴사하는 이유를 연구한다. 이에 기업은 직원이 퇴사하기 전에 면담을 하거나 설문조사를 통해서 연구에 필요한 데이터를 얻는다. 하지만 설문조사에서는 직원들이 직장 생활을 하는 데에 불리할 수도 있는 의견을 드러내려고 하지 않아 정확한 결과를 얻기 힘든 것이 현실이다. 한편, 한국노동연구원에서 발표한 자료에 따르면 기업이 요구하는 최소 학력 수준과 직원의 학력 수준 간의 차이가 클수록 이직 경향이 커진다. 따라서 본 연구에서는 한국노동연구원의 자료에 착안하여, 직원이 가지고 있는 객관적 데이터인 전공, 교육수준, 재직 중인 회사 유형 등의 데이터를 기반으로 직원의 퇴사 여부를 예측하고자 한다. 퇴사 예측 모델을 생성하기 위해 Decision Tree, XGBoost, kNN, SVM을 활용하였으며 각각의 성능을 비교했다. 이 결과, 지금까지 설문조사로 진행되었던 연구에서 파악하지 못한 다양한 요인을 알아낼 수 있었다. 이를 통해 기업이 퇴사 예측 모델을 이용하여 직원이 퇴사하기 전에 미리 이를 인지하고 방지하는 데에 도움을 줄 수 있을 것으로 예상된다.