A Hybrid PCA, MPK Method for Multi-Objective Human Resource Analytics in Plantation Management

Gilang  Ramadhan; Rahmad Syah; Ahmad  Rafiki

doi:10.29207/resti.v10i3.6903

Authors

Gilang Ramadhan Universitas Medan Area
Rahmad Syah Universitas Medan Area
Ahmad Rafiki Universitas Medan Area

DOI:

https://doi.org/10.29207/resti.v10i3.6903

Keywords:

human resource analytics, multi‐objective complexity prediction (MPK), multi‐objective optimization, plantation workforce management, principal component analysis (PCA)

Abstract

This study presents a hybrid framework combining Principal Component Analysis (PCA) and Multi‐Objective Complexity Prediction (MPK) to extract actionable insights from multidimensional human resource (HR) data in plantation companies. Initially, PCA reduces nine original HR variables including age, tenure, absenteeism, leave, harvest yield, and performance scores to four principal components that capture over 90 % of total variance. These reduced features form the solution space for MPK agents, each of which simultaneously optimizes three objectives: absenteeism risk (predicted via SVR), performance score (via linear regression), and workload imbalance. The model is tuned with 5-fold cross-validation on 80 % of the data, yielding MAE ≈ 6.5 days and R² ≈ 0.82 for absenteeism, MAE ≈ 4.2 points and R² ≈ 0.78 for performance, MAPE ≈ 15 % for workload imbalance, and a Pareto‐front hypervolume of ≈ 0.90. Validation on the remaining 20 % hold-out set confirms similar generalization (hypervolume ≈ 0.88). Sensitivity analysis with ± 10 % input noise demonstrates the approach’s robustness under moderate perturbations. These results illustrate that PCA–MPK can both streamline high-dimensional HR datasets and deliver reliable, multi-objective forecasts to inform strategic workforce planning.

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References

M. Habibi, Capitalism and Agrarian Change. London: Routledge, 2022. doi: 10.4324/9781003267348.

P. Bhattarai et al., “Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions,” IET Smart Grid, vol. 2, no. 2, pp. 141–154, Jun. 2019, doi: 10.1049/iet-stg.2018.0261.

Hauser, “Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis,” IEEE Trans Vis Comput Graph, vol. 23, no. 1, pp. 131–140, Jan. 2017, doi: 10.1109/TVCG.2016.2598470.

M. R. Shafie, H. Khosravi, S. Farhadpour, S. Das, and I. Ahmed, “A cluster-based human resources analytics for predicting employee turnover using optimized Artificial Neural Networks and data augmentation,” Decision Analytics Journal, vol. 11, p. 100461, Jun. 2024, doi: 10.1016/j.dajour.2024.100461.

M. Talebkhah, A. Sali, M. Gordan, S. J. Hashim, and F. Z. Rokhani, “Comprehensive Review on Development of Smart Cities Using Industry 4.0 Technologies,” IEEE Access, vol. 11, pp. 91981–92030, 2023, DOI: 10.1109/ACCESS.2023.3302262.

X. Wang and J. Zhi, “A machine learning-based analytical framework for employee turnover prediction,” Journal of Management Analytics, vol. 8, no. 3, pp. 351–370, Jul. 2021, doi: 10.1080/23270012.2021.1961318.

S. Verma, M. Pant, and V. Snasel, “A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems,” IEEE Access, vol. 9, pp. 57757–57791, 2021, doi: 10.1109/ACCESS.2021.3070634.

D. Truong, N. C. Le, H. N. The, and M.-H. Nguyen, “Machine Learning and Fuzzy Technique for Environmental Time Series Analysis,” 2022, pp. 295–319. DOI:10.1007/978-3-030-96429-0_14.

N. Salem and S. Hussein, “Data dimensional reduction and principal components analysis,” Procedia Comput Sci, vol. 163, pp. 292–299, 2019, doi: 10.1016/j.procs.2019.12.111.

Md. P. Uddin, Md. Al Mamun, and Md. A. Hossain, “PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification,” IETE Technical Review, vol. 38, no. 4, pp. 377–396, Jul. 2021, doi: 10.1080/02564602.2020.1740615.

R. B. Y. Syah, R. Muliono, M. Akbar Siregar, and M. Elveny, “An efficiency metaheuristic model to predicting customers churn in the business market with machine learning-based,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 2, p. 1547, Jun. 2024, doi: 10.11591/ijai.v13.i2.pp1547-1556.

F. L. Gewers et al., “Principal Component Analysis,” ACM Comput Surv, vol. 54, no. 4, pp. 1–34, May 2022, doi: 10.1145/3447755.

M. Greenacre, P. J. F. Groenen, T. Hastie, A. I. D’Enza, A. Markos, and E. Tuzhilina, “Principal component analysis,” Nat. Rev. Methods Primers, vol. 2, no. 1, Art. no. 100, Dec. 2022, doi: 10.1038/s43586-022-00184-w.

E. Saccenti, “A gentle introduction to principal component analysis using tea-pots, dinosaurs, and pizza,” Teaching Statistics, vol. 46, no. 1, pp. 38–52, Jan. 2024, doi: 10.1111/test.12363.

J. Lever, M. Krzywinski, and N. Altman, “Principal component analysis,” Nat. Methods, vol. 14, no. 7, pp. 641–642, Jul. 2017, doi: 10.1038/nmeth.4346.

I. T. Jolliffe and J. Cadima, “Principal component analysis: A review and recent developments,” Philos. Trans. R. Soc. A, vol. 374, no. 2065, Art. no. 20150202, Apr. 2016, DOI: 10.1098/rsta.2015.0202

R. B. Y. Syah, H. Satria, M. Elveny, and M. K. M. Nasution, “Complexity prediction model: a model for multi-object complexity in consideration to business uncertainty problems,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 6, pp. 3697–3705, Dec. 2023, doi: 10.11591/eei.v12i6.5380.

W. De Corte, P. R. Sackett, and F. Lievens, “Robustness, Sensitivity, and Sampling Variability of Pareto-Optimal Selection System Solutions to Address the Quality-Diversity Trade-Off,” Organ Res Methods, vol. 23, no. 3, pp. 535–568, Jul. 2020, doi: 10.1177/1094428118825301.

R. B. Syah, M. Elveny, R. F. Ananda, and M. K. M. Nasution, “A novel MPK optimization framework for financial data analysis incorporating complexity and uncertainty management,” Bull. Electr. Eng. Inform., vol. 14, no. 4, pp. 3121–3132, Aug. 2025, doi: 10.11591/eei.v14i4.9358.

D. Saxena, J. Kumar, A. K. Singh, and S. Schmid, “Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud,” IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 4, pp. 1313–1330, Apr. 2023, doi: 10.1109/TPDS.2023.3240567.

S. Bates, T. Hastie, and R. Tibshirani, “Cross-Validation: What Does It Estimate and How Well Does It Do It?,” J Am Stat Assoc, vol. 119, no. 546, pp. 1434–1445, Apr. 2024, doi: 10.1080/01621459.2023.2197686.

J. Qi, J. Du, S. M. Siniscalchi, X. Ma, and C.-H. Lee, “On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression,” IEEE Signal Process Lett, vol. 27, pp. 1485–1489, 2020, doi: 10.1109/LSP.2020.3016837.

D. S. K. Karunasingha, “Root mean square error or mean absolute error? Use their ratio as well,” Inf Sci (N Y), vol. 585, pp. 609–629, Mar. 2022, doi: 10.1016/j.ins.2021.11.036.

M. A. Stoffel, S. Nakagawa, and H. Schielzeth, “partR2”: partitioning R 2 in generalized linear mixed models,” PeerJ, vol. 9, p. e11414, May 2021, doi: 10.7717/peerj.11414.

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, p. e623, Jul. 2021, doi: 10.7717/peerj-cs.623.

A. Antoniadis, S. Lambert-Lacroix, and J.-M. Poggi, “Random forests for global sensitivity analysis: A selective review,” Reliab Eng Syst Saf, vol. 206, p. 107312, Feb. 2021, doi: 10.1016/j.ress.2020.107312.

S. Razavi et al., “The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support,” Environmental Modelling & Software, vol. 137, p. 104954, Mar. 2021, doi: 10.1016/j.envsoft.2020.104954.