Exploring Employee Working Productivity: Initial Insights from Machine Learning Predictive Analytics and Visualization

Exploring Employee Working Productivity: Initial Insights from Machine Learning Predictive Analytics and Visualization


  • Mohd Norhisham Razali Faculty of Business and Management, Universiti Teknologi MARA, Sarawak Branch, Samarahan Campus, MALAYSIA
  • Norizuandi Ibrahim College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Sarawak Branch, Samarahan Campus, MALAYSIA
  • Rozita Hanapi Faculty of Business and Management, Universiti Teknologi MARA, Sarawak Branch, Samarahan Campus, MALAYSIA
  • Norfarahzila Mohd Zamri Faculty of Business and Management, Universiti Teknologi MARA, Sarawak Branch, Samarahan Campus, MALAYSIA
  • Syaifulnizam Abdul Manaf Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor Malaysia




Employee Productivity, machine learning, prediction, visualization


Employee working productivity prediction is vital for effective resource allocation, increased productivity, and upholding a high-performance culture in organizations. However, predicting employee productivity and understanding the root factors influencing working performance pose significant challenges. Traditional human resource management practices often lack data-driven insights, resulting in poor resource allocation and productivity enhancement strategies. Subjective assessments, numerous assessment factors, and difficulties in interpreting predictive mechanisms add to the complexity of the task. To address these challenges, this research aims to develop a predictive model using machine learning techniques to determine employee productivity within organizations. Data from an academic institution were collected and pre-processed by encoding relevant features before applying various machine learning predictive models. Decision tree regressor, linear regression, MLP regressor, random forest regressor, SGD regressor, voting regressor, and Xgboost regressor were employed as predictive models. Ranker algorithms, including InfoGainAttributeEval, GainRatioAttributeEval, and CorrelationAttributeEval, were utilized to identify the most significant attributes affecting employee working performance. Additionally, descriptive analytics techniques were employed to visualize the data, extracting valuable insights and understanding the correlations among the features. Experimental results revealed that the linear regression model achieved the best performance in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), with values of 0.4878 and 0.4682, respectively. Thus, the linear regression model emerged as the most accurate predictor for employee productivity in the given organizational context. Based on these findings, it is recommended that organizations consider adopting linear regression for predicting employee productivity. The research findings also highlighted certain attributes that play an imperative role in predicting employee performance. Attributes such as "Department," "Actual Productive hours," "Internet Speed," and "COVID-19 adoption month" emerged as highly influential factors across multiple ranking techniques. The data visualization provided valuable insights into various aspects of employee performance, such as productivity trends before and after the pandemic, departmental performance, internet connectivity's impact on productivity, age-related trends, overtime distribution, and promotion rates. Organizations can use this data to inform workforce planning, address specific challenges in departments, and cultivate an inclusive work environment. By regularly assessing productivity data and implementing recommended strategies, organizations can enhance productivity, create a conducive work environment, and support employee well-being and growth. Future research can explore more advanced machine learning algorithms, incorporate time-series analysis for temporal dependencies, and expand data collection from diverse organizational settings to improve the generalizability of predictive models.


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Alhaj, T. A., Siraj, M., Zainal, A., Elshoush, H. T., & Elhaj, F. (2016). Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation. 1–18. https://doi.org/10.1371/journal.pone.0166017

Atatsi, E. A., Stoffers, J., & Kil, A. (2019). Factors affecting employee performance: a systematic literature review. Journal of Advances in Management Research, 16(3), 329–351. https://doi.org/10.1108/JAMR-06-2018-0052

Billson, R., Schiel, A., Yu-bo, Z., & Ming-duo, Y. (2021). Attributes selection using machine learning for analysing students ’ dropping out of university : a case study Attributes selection using machine learning for analysing students ’ dropping out of university : a case study. https://doi.org/10.1088/1757-899X/1031/1/012055

Dutt, M. I., & Saadeh, W. (2022). A Multilayer Perceptron ( MLP ) Regressor Network for Monitoring the Depth of Anesthesia. 2022 20th IEEE Interregional NEWCAS Conference (NEWCAS), 251–255. https://doi.org/10.1109/NEWCAS52662.2022.9842242

Erdebilli, B. (2022). Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste : A Case from Turkey. 7–9.

Hu, B. (2021). The application of machine learning in predicting absenteeism at work. Proceedings - 2021 2nd International Conference on Computing and Data Science, CDS 2021, 270–276. https://doi.org/10.1109/CDS52072.2021.00054

Jayadi, R., Jayadi, R., Firmantyo, H. M., Dzaka, M. T. J., Suaidy, M. F., & Putra, A. M. (2019). of Advanced Trends in Computer Science November and Employee Performance Prediction using Naïve Bayes. 8(6), 8–12.

Li, M. G. T., Lazo, M., Balan, A. K., & De Goma, J. (2021). Employee performance prediction using different supervised classifiers. Proceedings of the International Conference on Industrial Engineering and Operations Management, 6870–6876.

Maulud, D. H., & Abdulazeez, A. M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. 01(04), 140–147. https://doi.org/10.38094/jastt1457

Monisaa Tharani, S. K., & Vivek Raj, S. N. (2020). Predicting employee turnover intention in ITITeS industry using machine learning algorithms. Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020, 508–513. https://doi.org/10.1109/I-SMAC49090.2020.9243552

Munir, S., Seminar, K. B., Sukoco, H., & Buono, A. (2023). The Use of Random Forest Regression for Estimating Leaf Nitrogen Content of Oil Palm Based on Sentinel 1-A Imagery.

Naz, K., Siddiqui, I. F., Koo, J., Khan, M. A., & Qureshi, N. M. F. (2022). Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry. Applied Sciences (Switzerland), 12(20). https://doi.org/10.3390/app122010495

Patil, M. D. (2014). Effective Classification after Dimension Reduction : A. 4(7), 1–4.

Sabuj, H. H., Nuha, N. S., Gomes, P. R., Lameesa, A., & Alam, M. A. (2023). Interpretable Garment Workers’ Productivity Prediction in Bangladesh Using Machine Learning Algorithms and Explainable AI. 236–241. https://doi.org/10.1109/iccit57492.2022.10054863

Saputra, W., & Purwitasari, D. (2022). Fatigue Management: Machine Learning Application for Predicting Mining Worker Fatigue. 2022 International Conference on Information Technology Research and Innovation, ICITRI 2022, 117–122. https://doi.org/10.1109/ICITRI56423.2022.9970203

Sarker, A., Shamim, S. M., Shahiduz, M., Rahman, Z. M., Shahiduz Zama, M., & Rahman, M. (2018). Employee’s Performance Analysis and Prediction using K-Means Clustering & Decision Tree Algorithm. International Research Journal Software & Data Engineering Global Journal of Computer Science and Technology, 18(1), 7. https://computerresearch.org/index.php/computer/article/view/1660/1644

Shahani, N. M., Zheng, X., Liu, C., & Hassan, F. U. (2021). Developing an XGBoost Regression Model for Predicting Young ’ s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures. 9(October), 1–13. https://doi.org/10.3389/feart.2021.761990

Singh, G. (2022). Machine Learning Models in Stock Market Prediction. 3075(3), 18–28. https://doi.org/10.35940/ijitee.C9733.0111322

Sishi, M., & Telukdarie, A. (2021). The Application of Decision Tree Regression to Optimize Business Processes. Dm, 48–57.

Tambde, A., & Motwani, D. (2019). Employee churn rate prediction and performance using machine learning. International Journal of Recent Technology and Engineering, 8(2 Special Issue 11), 824–826. https://doi.org/10.35940/ijrte.B1134.0982S1119

Zainudin, M. N. S., Sulaiman, N., Mustapha, N., Perumal, T., & Mohamed, R. (2018). Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity. Turkish Journal of Electrical Engineering and Computer Sciences, 26(3), 1378–1389. https://doi.org/10.3906/elk-1709-138




How to Cite

Razali, M. N., Norizuandi Ibrahim, Hanapi, R., Mohd Zamri, N., & Abdul Manaf, S. (2023). Exploring Employee Working Productivity: Initial Insights from Machine Learning Predictive Analytics and Visualization . Journal of Computing Research and Innovation, 8(2), 235–245. https://doi.org/10.24191/jcrinn.v8i2.362



General Computing