Bibliometric Innovation: Towards Work Training Literature In The Era Of Digital Development

Imam Sucipto; Dadang Heri Kusumah

doi:10.31539/costing.v7i5.11675

Imam Sucipto
Dadang Heri Kusumah Universitas Pelita Bangsa

DOI: https://doi.org/10.31539/costing.v7i5.11675

Keywords: Bibliometric, Work Training, Digital Development, VOSviewer.

Abstract

The development of digital technology has had a significant impact on various aspects of life, including work training. In facing this digital era, bibliometric analysis has become a key tool for understanding the evolution of work training literature. This research proposes bibliometric innovations related to work training literature in the era of digital development. By utilizing bibliometric techniques, this research creates a roadmap illustrating trends, contributions, and collaboration patterns in the work training literature. The results of this bibliometric analysis provide an in-depth understanding of changes in work training literature triggered by the digital era. These findings can provide valuable guidance for researchers, practitioners, and policymakers to design training strategies that are responsive to technological developments. In conclusion, bibliometric innovation in the work training literature in the digital era provides a strong foundation for understanding the dynamics and capturing innovation opportunities in the context of future workforce training.

References

Gunawan, A. (2020). The Influence of Training and Work Experience on Employee Performance of Pt. Yi Shen Industrial. Business Management Analysis Journal (BMAJ), 3(1), 72–83. https://doi.org/10.24176/bmaj.v3i1.4435
Guu, K., Lee, K., Tung, Z., Pasupat, P., & ... (2020). Retrieval augmented language model pre-training. … on machine learning. http://proceedings.mlr.press/v119/guu20a.html?ref=https://githubhelp.com
He, K., Girshick, R., & Dollár, P. (2019). Rethinking imagenet pre-training. Proceedings of the IEEE/CVF …. http://openaccess.thecvf.com/content_ICCV_2019/html/He_Rethinking_ImageNet_Pre- Training_ICCV_2019_paper.html
Herdianto, R., Windyaningrum, N., Masruroh, B., & Setiawan, MA (2021). Philosophy of education and its development: bibliometric studies based on Scopus database [Philosophy of education and its development: bibliometric studies based on Scopus database]. Educational Belantika, 4(1), 44–56.
Joshi, M., Chen, D., Liu, Y., Weld, D.S., & ... (2020). Spanbert: Improving pre-training by representing and predicting spans. Transactions of the…. https://doi.org/10.1162/tacl_a_00300/43539
Karras, T., Aittala, M., Hellsten, J., Laine, S., & ... (2020). Training generative adversarial networks with limited data. Advances in neural…. https://proceedings.neurips.cc/paper/2020/hash/8d30aa96e72440759f74bd2306c1fa3d- Abstract.html
Karyono, & Gunawan, A. (2021). The Effect of Training and Compensation on Employee Performance at PT. Asahi Forge Indonesia. IKRA-ITH Economics, 4(2), 20–30.
Niati, DR, Siregar, ZME, & Prayoga, Y. (2021). The effect of training on work performance and career development: the role of motivation as intervening variable. In … International Research and …. researchgate.net. https://www.researchgate.net/profile/Zulkifli- Siregar/publication/351426754_The_Effect_of_Training_on_Work_Performance_and_ Career_Development_The_Role_of_Motivation_as_Intervening_Variable/links/6099a568299bf1ad8d908ce1/The-Effect-of-Training-on- Work-Perf
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., & Catanzaro, B. (2019). Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism. http://arxiv.org/abs/1909.08053
Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2020). Vl-Bert: Pre-Training of Generic Visual-Linguistic Representations. 8th International Conference on Learning Representations, ICLR 2020, 1–16.
Tan, M., & Le, Q. (2021). Efficientnetv2: Smaller models and faster training. International conference on machine learning. http://proceedings.mlr.press/v139/tan21a.html
Xie, Q., Dai, Z., Hovy, E., Luong, T., & ... (2020). Unsupervised data augmentation for consistency training. Advances in neural…. https://proceedings.neurips.cc/paper/2020/hash/44feb0096faa8326192570788b38c1d1- Abstract.html
Xie, Q., Luong, M.T., Hovy, E., & Le, Q.V. (2020). Self-training with noisy students improves imagenet classification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 10684–10695. https://doi.org/10.1109/CVPR42600.2020.01070
Zhang, J., Zhao, Y., Saleh, M., & Liu, P. J. (2020). PEGASUS: Pre-Training with extracted gap-sentences for abstractive summarization. 37th International Conference on Machine Learning, ICML 2020, PartF16814, 11265–11276.