Mellisa, Ira and Budiarto, Eka and Zahra, Amalia (2018) The Application of Data Mining in Human Resources Management to Build Data Mining Model for Employee Performance In Operator And Junior Management Level (Case Study: J Company). Masters thesis, Swiss German University.

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Abstract

Human resources management was designed by the company to optimize employee performance in order to achieve company goals. The company goals were determined by the top management and accompanied by some strategies that need to be accomplished. The employee performance was assessed by the company based on the ability of employees in achieving company goals. Hence, employee qualification theory needs to be adopted by the company so that the company could obtain an overview of employee performance. In the next stage, the company also need an effective method to predict performance, not only for employees but also for new applicants. The goals of this research are to get data mining models of the employee performance. By learning existing employee data, the performance of the new applicants could be predicted. The data mining model generated was derived from the application of data mining techniques on research materials. The study would produce the characteristic of new applicants who will give better performance than other applicants. The study used data from a company in Indonesia (J Company). The data mining techniques will be applied in the data of operators (such as admins, clerks, cashiers, machine operators, and security officers) and junior management (junior staffs, supervisors, and junior executives). The data mining technique used is decision tree. The decision tree technique was commonly used for a supervised learning data, while the support vector machine is a recent technique in data mining. The decision tree technique also has advantages compared others, because of its ability to produce information that is easy to understand. On the other hand, the support vector machine has a more accurate algorithm for predicting employee performance.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Data Mining; Employee Performance Prediction; Decision Tree; Support Vector Machine
Subjects:	H Social Sciences > HF Commerce > HF5549 Personnel management Q Science > QA Mathematics > QA76 Computer software >
Divisions:	Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User:	Astuti Kusumaningrum
Date Deposited:	06 Jun 2020 15:05
Last Modified:	06 Jun 2020 15:05
URI:	http://repository.sgu.ac.id/id/eprint/784

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