A Novel Classification Model for Employees Turnover Using Neural Network to Enhance Job Satisfaction in Organizations
The most important challenge facing modern organizations is to keep their employees as valuable assists. Employee turnover is one of these challenges. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. The proposed model is based on m...
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| Format: | Article |
| Language: | English |
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University of Zagreb, Faculty of organization and informatics
2021-01-01
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| Series: | Journal of Information and Organizational Sciences |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/392311 |
| _version_ | 1797206828440879104 |
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| author | Tarig Mohamed Ahmed |
| author_facet | Tarig Mohamed Ahmed |
| author_sort | Tarig Mohamed Ahmed |
| collection | DOAJ |
| description | The most important challenge facing modern organizations is to keep their employees as valuable assists. Employee turnover is one of these challenges. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. The proposed model is based on machine learning algorithms. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84% and AUC (ROC) of 74%. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner and setting proactive plans to keep them. Besides the model, three important features should be dealt with carefully as Over Time, Job Level, Monthly Income. |
| first_indexed | 2024-04-24T09:13:13Z |
| format | Article |
| id | doaj.art-ec88cfa4e24d4c5a9edefb2e21a456a6 |
| institution | Directory Open Access Journal |
| issn | 1846-3312 1846-9418 |
| language | English |
| last_indexed | 2024-04-24T09:13:13Z |
| publishDate | 2021-01-01 |
| publisher | University of Zagreb, Faculty of organization and informatics |
| record_format | Article |
| series | Journal of Information and Organizational Sciences |
| spelling | doaj.art-ec88cfa4e24d4c5a9edefb2e21a456a62024-04-15T17:29:06ZengUniversity of Zagreb, Faculty of organization and informaticsJournal of Information and Organizational Sciences1846-33121846-94182021-01-0145236137410.31341/jios.45.2.1A Novel Classification Model for Employees Turnover Using Neural Network to Enhance Job Satisfaction in OrganizationsTarig Mohamed Ahmed01: Prof - Department of IT, King Abdul- Aziz University, KSA. 2: Department of Computer Sciences, University of Khartoum, Khartoum, SudanThe most important challenge facing modern organizations is to keep their employees as valuable assists. Employee turnover is one of these challenges. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. The proposed model is based on machine learning algorithms. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84% and AUC (ROC) of 74%. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner and setting proactive plans to keep them. Besides the model, three important features should be dealt with carefully as Over Time, Job Level, Monthly Income.https://hrcak.srce.hr/file/392311Employee TurnoverJob SatisfactionMachine LearningClassification |
| spellingShingle | Tarig Mohamed Ahmed A Novel Classification Model for Employees Turnover Using Neural Network to Enhance Job Satisfaction in Organizations Journal of Information and Organizational Sciences Employee Turnover Job Satisfaction Machine Learning Classification |
| title | A Novel Classification Model for Employees Turnover Using Neural Network to Enhance Job Satisfaction in Organizations |
| title_full | A Novel Classification Model for Employees Turnover Using Neural Network to Enhance Job Satisfaction in Organizations |
| title_fullStr | A Novel Classification Model for Employees Turnover Using Neural Network to Enhance Job Satisfaction in Organizations |
| title_full_unstemmed | A Novel Classification Model for Employees Turnover Using Neural Network to Enhance Job Satisfaction in Organizations |
| title_short | A Novel Classification Model for Employees Turnover Using Neural Network to Enhance Job Satisfaction in Organizations |
| title_sort | novel classification model for employees turnover using neural network to enhance job satisfaction in organizations |
| topic | Employee Turnover Job Satisfaction Machine Learning Classification |
| url | https://hrcak.srce.hr/file/392311 |
| work_keys_str_mv | AT tarigmohamedahmed anovelclassificationmodelforemployeesturnoverusingneuralnetworktoenhancejobsatisfactioninorganizations AT tarigmohamedahmed novelclassificationmodelforemployeesturnoverusingneuralnetworktoenhancejobsatisfactioninorganizations |