PREDICTION OF EMPLOYEE PROMOTION USING HYBRID SAMPLING METHOD WITH MACHINE LEARNING ARCHITECTURE - Abstract and Method Summary

Aug 5, 2026

Employee promotion is crucial for organizations because it supports employee development, boosts loyalty, and helps reduce turnover. This study predicts employee promotion using historical promotion data with a hybrid sampling strategy integrated into machine learning models. Eight algorithms are compared, while imbalanced data is addressed using SMOTE+ENN and SMOTE+Tomek. Feature selection is conducted with RFE-RFC, EVR-PCA, and RFI-ECT, then models are evaluated using precision, recall, and F1-score.

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Abstract

Introduction

Malaysian Journal of Computing, 8 (1): 1264-1286, 2023 Copyright © UiTM Press eISSN: 2600-8238 PREDICTION OF EMPLOYEE PROMOTION USING HYBRID SAMPLING METHOD WITH MACHINE LEARNING ARCHITECTURE Shahidan bin Shafie1, Soek Peng Ooi2, Khai Wah Khaw3* 1,2,3*School of Management Universiti Sains Malaysia, 11800 Minden, Pulau Pinang. [1](1shahidan@usm.my)[shahidan@usm.my](1shahidan@usm.my), [2](2soekpeng915@student.usm.my)[soekpeng915@student.usm.my](2soekpeng915@student.usm.my),3*[khaiwah@usm.my](khaiwah@usm.my) ABSTRACT Employee promotion plays an important role in an organization. It aids to inspire employees to grow and develop their skills, thus increase employee loyalty and reduce the turnover rate. This study predicts employee job promotion based on employee promotion data by using a hybrid sampling method with machine learning. The purpose of this study is to accelerate the promotion process and share the important features that might be determined when promoting an employee. In this study, there are eight machine learning algorithms have been used, such as Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Naïve Bayes, Adaptive Boosting Classifier, and Extreme Gradient Boost. The purpose of using eight machine learning algorithms is to find out the most suitable model to predict employee promotion. Additionally, hybrid sampling methods like Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbor (SMOTE+ENN) and Synthetic Minority Oversampling Technique combined with Tomek Link (SMOTE+Tomek) were adopted. These two techniques are to cure the imbalanced dataset. For the importance of feature selection, the Recursive Feature Elimination method with Random Forest Classifier model (RFE-RFC), Explained Variance Ratio method with Principal Component Analysis (EVR-PCA), and the Rank Feature Importance method with Extra Classifier Tree model (RFI-ECT) is applied. The first 5, 8, and 12 features are selected based on the RFI-ECT to train the machine learning algorithms. As a result, the model is evaluated by precision, recall, and F1-score. In conclusion, the top five rank feature importance methods with the Extra Classifier Tree model are region, department, previous year rating, KPIs met and above 80%, and award won. The results suggest that SMOTE+ENN and Extreme Gradient Boost with eight features have the highest-performing model in this study. Keywords: Employee Promotion Prediction, Hybrid Sampling, Imbalanced Data Machine Learning, Received for review: 23-11-2022; Accepted: 23-03-2023; Published: 10-04-2023 DOI: 10.24191/mjoc.v8i1.18456 1. Introduction Employee promotion means the ascension of an employee to higher ranks like a salary increase, higher status, more benefits will receive, and job responsibilities will become heavy. Employees are most motivated by this duty because it is the greatest honour for their loyalty This is an open access article under the CC BY-SA license ([https://creativecommons.org/licenses/by-sa/3.0/](https://creativecommons.org/licenses/by-sa/3.0/)). Shafie et al., Malaysian Journal of Computing, 8 (1): 1264-1286, 2023 and dedication to the organization (Jyoti, 2022). Promoting an employee will take a long time and need to collect data or feedback and analyse the data. It will increase the workload of the human resource (HR) team. Human Resource Analytics (HRA) is a variety of tools, technologies, and methods for acquiring, saving, retrieving, and interpreting data to assist business users in making better choices to reduce the HR team’s workload (Bandi et al., 2021; Jain & Bhushan, 2020) . For example, the HR team used HRA to estimate the requirement of human resources in recruitment, training, development, retention, promotion, transfer, performance appraisal, retirement and others. (Kakulapati et al., 2020) . The goal is to increase the quality of peoplerelated decisions so that individuals an

FAQs

Why is employee promotion important in organizations?

Employee promotion motivates employees to grow and develop skills, increases loyalty, and reduces turnover by adding recognition and greater responsibilities.

Which hybrid sampling methods are used to handle imbalanced data?

The study uses SMOTE+ENN and SMOTE+Tomek to correct the imbalanced dataset before model training.

How are features selected and how is model performance evaluated?

Feature selection uses RFE-RFC, EVR-PCA, and RFI-ECT (with top-ranked features chosen for training). Model performance is measured using precision, recall, and F1-score.

Aug 5, 2026

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