Prediction of Employee Recruitment Selection in Indonesia Pharmaceutical Company Using Backpropagation Networks:

Apfia Normalita; Jek Siang jong; Halim Budi Santoso

doi:10.29207/resti.v9i5.6303

Authors

Apfia Normalita Universitas Kristen Duta Wacana
Jek Siang jong Universitas Kristen Duta Wacana
Halim Budi Santoso Universitas Kristen Duta Wacana

DOI:

https://doi.org/10.29207/resti.v9i5.6303

Keywords:

backpropagation, deep learning, employee selection model, multi-layer perception, recruitment

Abstract

PT K-24 Indonesia is one of the foremost companies in Indonesia, with a primary focus on distributing pharmaceutical products and healthcare services. During the last 2 years, PT K-24 has received more than 110,000 job applicants, with various position vacancies offered. The recruitment process begins with registration, online tests, and interviews. The need for manpower increases annually. More attention is required to select prospective employees who match the selection criteria. However, during the process, PT K-24 found that the recruitment process was less efficient because the applicants did not meet the company’s criteria. To overcome this problem, it is necessary to create a recommendation system for candidate selection. This study developed a recommendation using the multi-layer perceptron method, namely backpropagation. According to the prior literature, this method effectively reduces the error rate of prediction and recommendation results. This study also found that relevant data, the number of input parameters is not big enough, and the minimum network model can make better predictions with a considerable mean square error of 0.029. Our study contributes to the methodological approach by implementing real-world problems and measuring additional parameters that fit the selection requirements

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