A Combined Approach for Predicting Employees’ Productivity based on Ensemble Machine Learning Methods

Ruba Obiedat, Sara Amjad Toubasi


Garment industrial sector is one of the most important business sectors in the world. It presents the lifeblood for many countries’ economy. The demanding of garment merchandise in accretion year over year. There are many key factors affecting the performance of this sector including the employee productivity. This research proposes a hybrid approach which aims to predict the productivity performance of garment employees by combining different classification algorithms such as J48, random forest (RF), Radial Base Function network (RBF), Multilayer Perceptron (MLP), Naïve bayes (NB), and Support vector machine (SVM) with ensemble learning algorithms (Adaboost and bagging) on garment employee productivity dataset. This work monitors three major evaluation metrics including, accuracy, Mean Absolute Error value (MAE) and Root Mean Square Error (RMSE). The results show that RF outperforms the other algorithms with accuracy of 0.983 and MAE of 0097. In addition, the results prove that applying Adaboost ensemble algorithm with J48 enhances the classification performance with accuracy of 0.991 and MAE of 0.0101.

Full Text:



Zhang, Y., New advances in machine learning. 2010: BoD–Books on Demand.

Mahesh, B., Machine Learning Algorithms -A Review. 2019.

Mahesh, B., Machine Learning Algorithms-A Review.

International Journal of Science and Research (IJSR).[Internet], 2020. 9: p. 381-386.

Ozcift, A. and A. Gulten, Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Computer methods and programs in biomedicine, 2011. 104(3): p. 443-451.

Joutou, T. and K. Yanai. A food image recognition system with multiple kernel learning. in 2009 16th IEEE International Conference on Image Processing (ICIP). 2009. IEEE.

Fang, W., et al., Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going. IEEE Access, 2021. 9: p. 9777-9784.

Feng, W., W. Huang, and J. Ren, Class imbalance ensemble learning based on the margin theory. Applied Sciences, 2018. 8(5): p. 815.

Lemmens, A. and C. Croux, Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 2006. 43(2): p. 276-286.

Hearle, C., Skills, employment and productivity in the garments and construction sectors in Bangladesh and elsewhere. London: OPM, 2016.

Chowdhury, N.A., et al., A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry. Journal of Retailing and Consumer Services, 2019. 51: p. 102-113.

Hanaysha, J., Testing the effects of employee empowerment, teamwork, and employee training on employee productivity in higher education sector. International Journal of Learning and Development, 2016. 6(1): p. 164-178.

Harfoushi, O. and R. Obiedat, E-Training acceptance factors in business organizations. International Journal of Emerging Technologies in Learning (iJET), 2011. 6(2): p. 15-18.

Evans, W.R. and W.D. Davis, High-performance work systems as an initiator of employee proactivity and flexible work processes. Organization Management Journal, 2015. 12(2): p. 64-74.

Harfoushi, O., R. Obiedat, and S. Khasawneh, E-learning adoption inside Jordanian organizations from change management perspective. International Journal of Emerging Technologies in Learning (iJET), 2010. 5(2): p. 49-60.

Alam, M., R. Alias, and M. Azim, Social Compliance Factors (SCF) Affecting Employee Productivity (EP): An Empirical Study on RMG Industry in Bangladesh. 2018. 10: p. 87-96.

Bhatia, K., S. Arora, and R. Tomar. Diagnosis of diabetic retinopathy using machine learning classification algorithm. in 2016 2nd international conference on next generation computing technologies (NGCT). 2016. IEEE.

Kruppa, J., et al., Consumer credit risk: Individual probability estimates using machine learning. Expert Systems with Applications, 2013. 40(13): p. 5125-5131.

Balla, I., S. Rahayu, and J.J. Purnama, GARMENT EMPLOYEE PRODUCTIVITY PREDICTION USING RANDOM FOREST. Jurnal Techno Nusa Mandiri, 2021. 18(1): p. 49-54.

Attygalle, D. and G. Abhayawardana, Employee Productivity Modelling on a Work From Home Scenario During the Covid-19 Pandemic: A Case Study Using Classification Trees. Journal of Business and Management Sciences, 2021. 9(3): p. 92-100.

Ďurica, M., J. Frnda, and L. Svabova, Decision tree based model of business failure prediction for Polish companies. Oeconomia Copernicana, 2019. 10: p. 453-469.

Mahoto, N., et al., An Intelligent Business Model for Product Price Prediction Using Machine Learning Approach. 2021. 30: p. 147-159.

Sorostinean, R., A. Gellert, and B.-C. Pirvu, Assembly Assistance System with Decision Trees and Ensemble Learning. Sensors, 2021.

(11): p. 3580.

Saad, H., Use Bagging Algorithm to Improve Prediction Accuracy for Evaluation of Worker Performances at a Production Company. arXiv preprint arXiv:2011.12343, 2020.

El Hassani, I., C. El Mazgualdi, and T. Masrour, Artificial intelligence and machine learning to predict and improve efficiency in manufacturing industry. arXiv e-prints, 2019: p. arXiv: 1901.02256.

De Lucia, C., P. Pazienza, and M. Bartlett, Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe. Sustainability, 2020. 12(13): p. 5317.

Ihya, R., et al. J48 algorithms of machine learning for predicting user's the acceptance of an E-orientation systems. in Proceedings of the 4th International Conference on Smart City Applications. 2019.

Uma Mahesh, J., et al., Analysis of J48 Algorithm in Classification-Ebola Virus. 2021.

Narayanan, V., I. Arora, and A. Bhatia. Fast and accurate sentiment classification using an enhanced Naive Bayes model. in International Conference on Intelligent Data Engineering and Automated Learning. 2013. Springer.

Nazzal, J., I. El-Emary, and S. Najim, Multilayer Perceptron Neural Network (MLPs) For Analyzing the Properties of Jordan Oil Shale. World Applied Sciences Journal, 2008. 5.

Delashmit, W.H. and M.T. Manry. Recent developments in multilayer perceptron neural networks. in Proceedings of the seventh Annual Memphis Area Engineering and Science Conference, MAESC. 2005.

Leung, H., T. Lo, and S. Wang, Prediction of noisy chaotic time series using an optimal radial basis function neural network. IEEE Transactions on Neural Networks, 2001. 12(5): p. 1163-1172.

Kumari, V.A. and R. Chitra, Classification of diabetes disease using support vector machine. International Journal of Engineering Research and Applications, 2013. 3(2): p. 1797-1801.

Yaman, E. and A. Subasi, Comparison of bagging and boosting ensemble machine learning methods for automated EMG signal classification. BioMed research international, 2019. 2019.

Bühlmann, P., Bagging, Boosting and Ensemble Methods. Handbook of Computational Statistics, 2012.

Imran, A.A., M.S. Rahim, and T. Ahmed, Mining the Productivity Data of Garment Industry. International Journal of Business Intelligence and Data Mining, 2021. 1.

Vujović, Ž.Đ., Classification Model Evaluation Metrics.

Chai, T. and R.R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geosci. Model Dev., 2014. 7(3): p. 1247-1250.

DOI: https://doi.org/10.31449/inf.v46i5.3839

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.