Application of Predictive Analytics To Improve The Hiring Process In A Telecommunications Company

Luh Putu Saraswati Devia Jayanti, Meditya Wasesa

Abstract


Industry 4.0 refers to the increasing tendency towards automation and data exchange in technologies like Big Data and AI. The existence of technology means telecommunication companies have to adapt. Therefore, it takes great people so that the company can continue to survive. The problem that companies often face in hiring great people is that it costs a lot and takes a long time to recruit. Predictive analysis can assist in identifying system issues and solutions. This study aims to develop predictive analytics that can improve recruitment screening based on CVs and find the best predictive model for the company to reduce costs and long recruitment cycles using technology. The authors built an analytical prediction model in four stages: data collection, data preprocessing, model building, and model evaluation. This technique uses Random Forest and Naive Bayes classification algorithms. Both systems properly predicted more data sets with 70% accuracy, 70% precision, and a recall rate above 80%. Compared between the two techniques, Random Forest outperforms Naive Bayes for this predictive model. A lot of people are talking about predictive analytics for hiring, but there aren't many data mining frameworks that can help to find rules based on the CVs of people who have worked for companies before.

Keywords: Recruitment, Human Resource, HR Analytic, Predictive Analytic, Random Forest, Naïve Bayes



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DOI: http://dx.doi.org/10.24014/coreit.v8i1.16915

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