Jobs Prediction Model based on Myanmar Labor Force Survey by using Artificial Neural Network

Abstract

Nowadays, job prediction is an essential task for every organization because of the influence of job fitting or job suitability. Job fit is a concept that refers to how well an employee is suited for his or her position and which can have a positive impact on the organization's morale. Therefore, the job prediction model is needed for both employees and employers. In this paper, the job prediction model for Myanmar employment is proposed and implemented by using the Artificial Neural Network (ANN) and back-propagation algorithm. The proposed model learns the real dataset of the Myanmar Labor which is collected from the Department of Labor, Ministry of Labor, and Immigration. The International Standard Classification of Occupations (ISCO) defines ten main job classes. Therefore, the proposed model can predict these ten job classes. According to the experimental result, the proposed model achieved the best result with an accuracy of 97%. The prediction result of the proposed system can also help not only decision-makers who worked in the management level of private or government organizations but also job seekers.