Analysis of Employee Work Stress Using CRISP-DM to Reduce Work Stress on Reasons for Employee Resignation

Authors

  • Emral Hakim Institut Teknologi Sepuluh Nopember
  • Ahmad Muklason Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.32734/jocai.v8.i2-14615

Keywords:

Work Stress, Descriptive Analysis, Diagnostic Analysis, K-Modes Clustering, SECI

Abstract

Internal audit activities at EPC companies have found a trend of increasing work stress as a reason for employee resignation in the period Q4 2021 - Q1 2023. In implementing ISO 45001:2015 this must be controlled because it is a psychological occupational disease. For this reason, a work stress survey was carried out, the results of which were reviewed using Cross Industry Standard Process for Data Mining (CRISP-DM). Descriptive analysis found a maximum ratio of moderate stress of 66%, light stress of 39%, and severe stress of 9% with a risk matrix in Medium (yellow area). Descriptive analysis found a maximum ratio of moderate stress of 66%, light stress of 39%, and severe stress of 9% with a risk matrix in Medium (yellow area). Diagnostic analysis found a total of 19 questionnaires that affected severe stress and moderate stress. Cluster K-Modes shows 3 clusters being centroids with principal component values explaining around 4.92% of the original feature variance. The deployment of work stress control is carried out through focus group discussion to formulate Socialization, Externalization, Combination, Internalization (SECI) as a follow-up program for organization.

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References

N. Ghodrati, T. W. Yiu, S. Wilkinson, and M. Shahbazpour, “A new approach to predict safety outcomes in the construction industry,†Saf Sci, vol. 109, pp. 86–94, Nov. 2018, doi: 10.1016/j.ssci.2018.05.016.

H. Alqudah et al., “The impact of empowering internal auditors on the quality of electronic internal audits: A case of Jordanian listed services companies,†International Journal of Information Management Data Insights, vol. 3, no. 2, Nov. 2023, doi: 10.1016/j.jjimei.2023.100183.

Q. Liang, Z. Zhou, G. Ye, and L. Shen, “Unveiling the mechanism of construction workers’ unsafe behaviors from an occupational stress perspective: A qualitative and quantitative examination of a stress–cognition–safety model,†Saf Sci, vol. 145, Jan. 2022, doi: 10.1016/j.ssci.2021.105486.

S. Zhang, R. Y. Sunindijo, S. Frimpong, and Z. Su, “Work stressors, coping strategies, and poor mental health in the Chinese construction industry,†Saf Sci, vol. 159, Mar. 2023, doi: 10.1016/j.ssci.2022.106039.

M. Mavroulidis et al., “Occupational health and safety of multinational construction companies through evaluation of corporate social responsibility reports,†J Safety Res, vol. 81, pp. 45–54, Jun. 2022, doi: 10.1016/j.jsr.2022.01.005.

A. Fernandes, M. Figueiredo, J. Ribeiro, J. Neves, and H. Vicente, “Psychosocial Risks Assessment in Cryopreservation Laboratories,†Saf Health Work, vol. 11, no. 4, pp. 431–442, Dec. 2020, doi: 10.1016/j.shaw.2020.07.003.

V. Golinko, S. Cheberyachko, O. Deryugin, O. Tretyak, and O. Dusmatova, “Assessment of the Risks of Occupational Diseases of the Passenger Bus Drivers,†Saf Health Work, vol. 11, no. 4, pp. 543–549, Dec. 2020, doi: 10.1016/j.shaw.2020.07.005.

W. Umer, “Simultaneous monitoring of physical and mental stress for construction tasks using physiological measures,†Journal of Building Engineering, vol. 46, Apr. 2022, doi: 10.1016/j.jobe.2021.103777.

Kementerian Ketenagakerjaan Republik Indonesia, “Peraturan Menteri Ketenagakerjaan Nomor 5 Tahun 2018 Tentang Keselamaan dan Kesehatan Kerja Lingkungan Kerja,†Jakarta, 2018.

IBM Corporation, “IBM SPSS Modeler CRISP-DM Guide,†Internet News Group.

C. C. & R. C. K. Aggarwal, Data Clustering Algorythms and Applications, 1st ed. Florida: Chapman and Hall/CRC, 2013.

E. B. Ewin Karman Nduru, “Implementasi Algoritma K-Modes Untuk Menentukan Strategi Marketing STMIK Budi Darma,†in Konferensi Nasional Teknologi Informasi dan Komputer, Medan: STMIK Budi Darma, 2018, pp. 12–19.

N. B. Mendoza, E. C. K. Cheng, and Z. Yan, “Assessing teachers’ collaborative lesson planning practices: Instrument development and validation using the SECI knowledge-creation model,†Studies in Educational Evaluation, vol. 73, Jun. 2022, doi: 10.1016/j.stueduc.2022.101139.

A. Lane, “Towards a theory of organizational storytelling for public relations: An engagement perspective,†Public Relat Rev, vol. 49, no. 1, Mar. 2023, doi: 10.1016/j.pubrev.2023.102297.

A. Kemp, R. Gravois, H. Syrdal, and E. McDougal, “Storytelling is not just for marketing: Cultivating a storytelling culture throughout the organization,†Bus Horiz, vol. 66, no. 3, pp. 313–324, May 2023, doi: 10.1016/j.bushor.2023.01.008.

A. M. W. Leong, S.-S. Yeh, Y. Zhou, C.-W. Hung, and T.-C. Huan, “Exploring the influence of historical storytelling on cultural heritage tourists’ value co-creation using tour guide interaction and authentic place as mediators,†Tour Manag Perspect, vol. 50, p. 101198, Jan. 2024, doi: 10.1016/j.tmp.2023.101198.

D. Andino Ardi, K. Ulfah Naila El Muna, D. Dwimartha, and L. Dysi Setiawati, “Gambaran Tingkat Stres pada Pekerja PT. Sucofindo Cabang Surabaya Tahun 2022,†SEHATRAKYAT (Jurnal Kesehatan Masyarakat) , vol. 2, no. 2, pp. 221–228, 2023, doi: 10.54259/sehatrakyat.v2i2.1659.

M. Marchelli, G. Coltrinari, G. Alfaro Degan, and D. Peila, “Towards a procedure to manage safety on construction sites of rockfall protective measures,†Saf Sci, vol. 168, Dec. 2023, doi: 10.1016/j.ssci.2023.106307.

Shashi Kant Sharma, “Guidelines on Data Analytics,†2017.

A. Golzari Oskouei, M. A. Balafar, and C. Motamed, “FKMAWCW: Categorical fuzzy k-modes clustering with automated attribute-weight and cluster-weight learning,†Chaos Solitons Fractals, vol. 153, Dec. 2021, doi: 10.1016/j.chaos.2021.111494.

S. S. Khan and A. Ahmad, “Cluster center initialization algorithm for K-modes clustering,†Expert Syst Appl, vol. 40, no. 18, pp. 7444–7456, 2013, doi: 10.1016/j.eswa.2013.07.002.

R. J. Kuo, Y. R. Zheng, and T. P. Q. Nguyen, “Metaheuristic-based possibilistic fuzzy k-modes algorithms for categorical data clustering,†Inf Sci (N Y), vol. 557, pp. 1–15, May 2021, doi: 10.1016/j.ins.2020.12.051.

Y. Yati, “Idea generation techniques in pop-up tourism labs,†Annals of Tourism Research Empirical Insights, vol. 4, no. 1, May 2023, doi: 10.1016/j.annale.2023.100096.

Published

2024-07-31

How to Cite

Emral Hakim, & Ahmad Muklason. (2024). Analysis of Employee Work Stress Using CRISP-DM to Reduce Work Stress on Reasons for Employee Resignation. Data Science: Journal of Computing and Applied Informatics, 8(2), 75-87. https://doi.org/10.32734/jocai.v8.i2-14615