Pengaruh Adopsi Artificial Intelligence dan Self-Learning terhadap Kinerja Pegawai pada Sektor Publik
DOI:
https://doi.org/10.61231/nc1sxa85Keywords:
Artificial Intelligence (AI), Self-Learning, Kinerja Pegawai, Sektor PublikAbstract
This study aims to examine the effects of Artificial Intelligence (AI) adoption and self-learning on the performance of public sector employees at the Pidie Jaya Regent's Office. This study employed a quantitative approach using a survey method involving all 66 employees through a saturated sampling technique. Data were analyzed using multiple linear regression with SPSS. The results indicate that Artificial Intelligence (AI) adoption has a positive and significant effect on employee performance (β = 0.387; t = 4.680; Sig. = 0.000). Likewise, self-learning has a positive and significant effect (β = 0.521; t = 7.017; Sig. = 0.000) and is identified as the most influential variable. Simultaneously, both variables have a positive and significant effect on employee performance (F = 163.734; Sig. = 0.000), with an Adjusted R² of 0.834, indicating that 83.4% of the variance in employee performance is explained by Artificial Intelligence (AI) adoption and self-learning, while the remaining 16.6% is explained by other factors beyond the research model.
References
Abogunrin, O., Adeyemi, T., & Ibrahim, A. (2025). Artificial intelligence adoption and organizational transformation. Journal of Digital Innovation, 12(1), 45–60.
Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. L. (2000). Manufacturing advantage: Why high-performance work systems pay off. Cornell University Press.
Armstrong, M. (2023). Armstrong's handbook of human resource management practice (16th ed.). Kogan Page.
Babashahi, L., et al. (2024). AI in the workplace: A systematic review of skill demands and organizational outcomes. Human Resource Development Review.
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). Sage Publications.
Dessler, G. (2022). Human resource management (16th ed.). Pearson.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). So what if ChatGPT wrote it Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
Ghozali, I. (2021). Aplikasi analisis multivariate dengan program IBM SPSS 26 (Edisi ke-10). Badan Penerbit Universitas Diponegoro.
Goswami, S., Sharma, R., & Singh, P. (2023). Artificial intelligence adoption in human resource management: Drivers and challenges. International Journal of Human Resource Studies, 13(2), 120–136.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate data analysis (9th ed.). Cengage Learning.
Hassan, B. F. Y., et al. (2025). Adoption of artificial intelligence in human resource management: A quantitative study using the technology acceptance model. International Journal of Human Resource Studies.
Hassan, M., Ahmed, S., & Rahman, K. (2024). Organizational readiness and artificial intelligence adoption in public organizations. Government Information Quarterly, 41(2), 101890.
Hein, A., & Urban, W. (2025). Self-directed learning and employee development in modern organizations. Human Resource Development Review, 24(1), 35–52.
Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Association Press.
Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., Schaufeli, W. B., de Vet, H. C. W., & van der Beek, A. J. (2014). Measuring individual work performance: Identifying and selecting indicators. Journal of Occupational and Environmental Medicine, 56(3), 331–337. https://doi.org/10.1097/JOM.0000000000000113
Lemmetty, S., & Collin, K. (2020). Self-directed learning as a practice of workplace learning: Interpretative repertoires of self-directed learning in ICT work. Journal of Workplace Learning, 32(6), 391–404.
Mangkunegara, A. A. A. P. (2022). Manajemen sumber daya manusia perusahaan. PT Remaja Rosdakarya.
Maurer, T. J., Weiss, E. M., & Barbeite, F. G. (2003). A model of involvement in work-related learning and development activity: The effects of individual, situational, motivational, and age variables. Journal of Applied Psychology, 88(4), 707–724. https://doi.org/10.1037/0021-9010.88.4.707
Mwita, K. M., & Kitole, F. A. (2025). Artificial intelligence and human resource management in public institutions. Public Organization Review, 25(1), 55–73.
Noe, R. A., Clarke, A. D. M., & Klein, H. J. (2023). Learning in the twenty-first-century workplace. Annual Review of Organizational Psychology and Organizational Behavior, 10, 1–29.
OECD. (2024). The impact of artificial intelligence on productivity, distribution and growth. OECD Publishing.
Panigrahi, S. (2025). Artificial intelligence in human resource management: Emerging trends and future directions. Human Resource Development Review, 24(1), 15–31.
Prakash, V., Sharma, A., & Kumar, R. (2024). AI-driven human resource management and organizational performance. International Journal of Organizational Analysis, 32(4), 678–695.
Puspitasari, D. (2024). Self-directed learning dan pengembangan kompetensi sumber daya manusia di era digital. Jurnal Manajemen Sumber Daya Manusia Indonesia, 8(2), 115–127.
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Robbins, S. P., & Judge, T. A. (2023). Organizational behavior (19th ed.). Pearson.
Sedarmayanti. (2022). Manajemen sumber daya manusia reformasi birokrasi dan manajemen pegawai negeri sipil. Refika Aditama.
Saputra, A. M., Maihani, S., Rizkina, A., & Mukhdasir, M. (2025). Digital Service Experience Moderating Satisfaction on Loyalty: Generational Comparison in Indonesian Fashion MSMEs. Business Review and Case Studies, 6(3), 408-408.
Sekaran, U., & Bougie, R. (2020). Research methods for business: A skill-building approach (8th ed.). John Wiley & Sons.
Singh, A., & Pandey, S. (2023). Artificial intelligence adoption in human resource management: A strategic perspective. Journal of Human Resource Management, 11(3), 201–216.
Sugiyono. (2023). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2022). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 46(2), 1025–1055.
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
Vithayaporn, S., Harfield, T., & Meyer, L. H. (2021). Workplace self-directed learning and employee performance development. International Journal of Training and Development, 25(3), 267–284.
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—Applications and challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Khalil Gibran, Fakhrurrazi Fakhrurrazi, Rio Putra Ramadhani, Mulia Andirfa, Al Mahfud Saputra

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.







