EXPERT SYSTEM DEVELOPMENT TO IDENTIFY EMPLOYEE PERSONALITY TYPES USING DEMPSTER SHAFER THEORY
Human resources are an important asset for the company to develop and realize the company's goals. One of the efforts to optimize the capacity of employees is to know their personality. Personality is the form possessed by an individual in behaving and all the characteristics that distinguish o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Kresnamedia Publisher
2022-06-01
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| Series: | Jurnal Riset Informatika |
| Subjects: | |
| Online Access: | https://ejournal.kresnamediapublisher.com/index.php/jri/article/view/389 |
| Summary: | Human resources are an important asset for the company to develop and realize the company's goals. One of the efforts to optimize the capacity of employees is to know their personality. Personality is the form possessed by an individual in behaving and all the characteristics that distinguish one individual from another. Knowing the personality of employees is important for the company and the employees themselves. Because by knowing a person's personality, the company can maximize the potential of employees and can place certain positions that suit the personality of the employee. This study aims to implement dempster-shafer theory on an inference engine in building an expert system to identify employee personality types. Dempster-shafer theory can perform probability calculations so that evidence can be carried out based on the level of confidence and logical reasoning. The system developed is able to identify the personality type of the employee through the nature or symptoms that exist in the employee. In addition, the system can display the results of the diagnosis with an explanation of the personality type, its nature in work and occupations or positions that are suitable for that personality type. Based on the results of the accuracy test obtained from the comparison of expert system diagnoses with the analysis of an expert, the accuracy value reaches 85%. |
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| ISSN: | 2656-1743 2656-1735 |