Decision Support Model for Employee Selection Based on Data Mining and Fuzzy Logic

Pah, Clarissa Elfira Amos; Utama, Ditdit Nugeraha

doi:10.3844/jcssp.2020.686.701

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Since Fuzzy theory was introduced [35], Fuzzy logic is seen as a useful approach for dealing with ambiguity and uncertainty, and it has been successfully used in a variety of industries. Fuzzy Inference Systems (FIS) have been widely used by scholars in particular in the field of Decision Support Systems, and some scholars [23,24,28,31] have also used a Fuzzy logic approach to deal with students' degree of preparation assessment, so in this research it is been decided to explore FIS for the assessment the students' competencies as described.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy model for assessing acquired competencies

Sheveleva,

Dobrynin,

Mastroianni

et al. 2023

E3S Web Conf.

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The recent pandemic and economic crises demonstrated that traditional educational approaches cannot face the today’s world challenges. A creative student-centered approach competency-based approach is becoming the leading educational trend around the world. In this regard, it is necessary to improve digital tools used at university to solve a wide range of new educational tasks. In this paper, a model that allows generating automatically the final testing of competencies is designed, along with the model of results evaluation with help of fuzzy logic. The presented model helps to assess the level of competencies through its components (understanding, having skill, ability), makes the assessment more flexible and adaptive and reduces teacher stress.

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Section: Methodsmentioning

confidence: 99%

Fuzzy model for assessing acquired competencies

Sheveleva,

Dobrynin,

Mastroianni

et al. 2023

E3S Web Conf.

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show abstract

“…This paper discussed how a company can recruit potential employees accurately by accessing historical data. Data Mining and Fuzzy Logic [27] approaches to do this task more easily and accurately. Here, Data mining was used to classify attributes of latent employees, and then Fuzzy logic is used to order employees who will be recruited.…”

Section: Related Workmentioning

confidence: 99%

Employee Promotion Prediction Using Improved AdaBoost Machine Learning Approach

Jafor,

Wadud,

Nur

et al. 2023

AJSE

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Employee promotion is an important aspect of the human resource management process. Due to different factors, it refers to the automatic improvement among the employees in an organization. Promoting employees from the lower level to the higher level brings a feeling of satisfaction among the employees. It improves their job satisfaction and motivation by providing more significant income, status, and responsibilities. By building up loyalty, promotion reduces employee attrition. Thus, it is difficult to accurately decide, whether an employee should or should not be promoted based on their current and past performance. So, human resource management does research about promotion, because there are a limited number of research about the finding of employee promotion prediction in the existing studies. First, to find the reasons for employee promotion, we need to analyze the research study for finding the factors which are related to the promotion. The aim of this research study is to implement an employee promotion prediction framework using machine learning. A modified AdaBoost classifier is used for automatic promotion prediction, and six machine learning techniques for instance, Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), XGBoost (XGB), and AdaBoost are applied in performance comparison. Through a complex assessment process, the performance of these supervised machine learning algorithms for predicting employee advancement is analyzed using assessment metrics on the employees' evaluation dataset for promotion prediction. The Artificial Neural Network (ANN) and AdaBoost model provide better results on this dataset than all traditional machine learning techniques. Finally, Our proposed modified AdaBoost approach outperformed all other methods evaluated with an accuracy of 95.30%.

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Role Balance Assignment Based on OCAT Method in Human Resource Planning

Que

2024

J. Info. Know. Mgmt.

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The core of human resources is the division of labour of enterprise employees. A human resource allocation method based on the E-CARGO model was designed to improve the development potential of enterprises and balance the preferences of managers and the task execution of employees in human resource allocation. This method was used to analyse the preferences of enterprise managers for employees and describe their preferences using the E-CARGO model. Then the OCAT method was used to mine the relationship between the team execution and the preferences of managers to find the balance between the two. The results showed that the experimental Scheme found the balance between the team execution and the preferences of managers. The original experimental scheme found three balance points, the improved scheme found three balance points, and the improved scheme found one balance point and one balance interval. Among the three experimental schemes, the improvement Scheme 1 achieved the highest execution ability and the shortest time. The research successfully analyses the relationship between the team execution and the preferences of managers in enterprise human resource assignment and puts forward a human resource assignment scheme that takes both into account.

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Decision Support Model for Employee Selection Based on Data Mining and Fuzzy Logic

Cited by 3 publications

References 12 publications

Fuzzy model for assessing acquired competencies

Fuzzy model for assessing acquired competencies

Employee Promotion Prediction Using Improved AdaBoost Machine Learning Approach

Role Balance Assignment Based on OCAT Method in Human Resource Planning

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