Tinjauan Bibliometrik Komprehensif tentang Crowdsourcing dalam Penelitian Ilmiah dan Dunia Akademik

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Loso Judijanto

Abstract

Penelitian ini bertujuan untuk melakukan tinjauan bibliometrik komprehensif mengenai crowdsourcing dalam penelitian ilmiah dan dunia akademik. Dengan menggunakan data dari basis data Scopus dan dianalisis menggunakan perangkat lunak VOSviewer, penelitian ini mengidentifikasi tren utama dalam pemanfaatan crowdsourcing, pola kolaborasi antarpeneliti, serta tantangan dan peluang yang muncul dalam penerapannya. Hasil analisis menunjukkan bahwa crowdsourcing semakin berkembang dalam berbagai disiplin ilmu, terutama dalam kecerdasan buatan, pembelajaran mesin, dan ilmu sosial. Kolaborasi akademik dalam penelitian ini didominasi oleh negara-negara maju seperti Amerika Serikat, Kanada, dan Hong Kong, yang memiliki jaringan penelitian yang luas. Meskipun crowdsourcing menawarkan berbagai manfaat, tantangan seperti validitas data, masalah etika, dan eksploitasi tenaga kerja digital masih menjadi perhatian utama. Dengan semakin berkembangnya teknologi seperti blockchain dan kecerdasan buatan, crowdsourcing memiliki potensi besar untuk terus berkontribusi dalam inovasi penelitian akademik serta meningkatkan keterlibatan masyarakat dalam ilmu pengetahuan.

Article Details

How to Cite
Judijanto, L. (2025). Tinjauan Bibliometrik Komprehensif tentang Crowdsourcing dalam Penelitian Ilmiah dan Dunia Akademik. Sanskara Manajemen Dan Bisnis, 3(02), 89–99. https://doi.org/10.58812/smb.v3i02.538
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References

Brabham, D. C. (2008). Crowdsourcing as a model for problem solving: An introduction and cases. Convergence, 14(1), 75–90.

Brabham, D. C. (2015). Crowdsourcing in the public sector. Georgetown University Press.

Brabham, D. C., Ribisl, K. M., Kirchner, T. R., & Bernhardt, J. M. (2014). Crowdsourcing applications for public health. American Journal of Preventive Medicine, 46(2), 179–187.

Consortium, E.-T., Van Deun, J., Mestdagh, P., Agostinis, P., Akay, Ö., Anand, S., Anckaert, J., Martinez, Z. A., Baetens, T., & Beghein, E. (2017). EV-TRACK: transparent reporting and centralizing knowledge in extracellular vesicle research. Nature Methods, 14(3), 228–232.

Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189–200.

Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96–104.

Ghezzi, A., Gabelloni, D., Martini, A., & Natalicchio, A. (2018). Crowdsourcing: a review and suggestions for future research. International Journal of Management Reviews, 20(2), 343–363.

Hammon, L., & Hippner, H. (2012). Crowdsourcing. Business & Information Systems Engineering, 4, 163–166.

Hossain, M., & Kauranen, I. (2015). Crowdsourcing: a comprehensive literature review. Strategic Outsourcing: An International Journal, 8(1), 2–22.

Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14(6), 176–183.

Kittur, A., Smus, B., Khamkar, S., & Kraut, R. E. (2011). Crowdforge: Crowdsourcing complex work. Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, 43–52.

Koivisto, J., & Hamari, J. (2019). The rise of motivational information systems: A review of gamification research. International Journal of Information Management, 45, 191–210.

Kraemer, M. U. G., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D. M., Group†, O. C.-19 D. W., Du Plessis, L., Faria, N. R., & Li, R. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493–497.

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., & Shamma, D. A. (2017). Visual genome: Connecting language and vision using crowdsourced dense image annotations. International Journal of Computer Vision, 123, 32–73.

Kuleshov, M. V, Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., & Lachmann, A. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research, 44(W1), W90–W97.

Litman, L., Robinson, J., & Abberbock, T. (2017). TurkPrime. com: A versatile crowdsourcing data acquisition platform for the behavioral sciences. Behavior Research Methods, 49(2), 433–442.

Oran, D. P., & Topol, E. J. (2020). Prevalence of asymptomatic SARS-CoV-2 infection: a narrative review. Annals of Internal Medicine, 173(5), 362–367.

Papanastasiou, Y., Bimpikis, K., & Savva, N. (2018). Crowdsourcing exploration. Management Science, 64(4), 1727–1746.

Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153–163.

Pion-Tonachini, L., Kreutz-Delgado, K., & Makeig, S. (2019). ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. NeuroImage, 198, 181–197.

Schenk, E., & Guittard, C. (2011). Towards a characterization of crowdsourcing practices. Journal of Innovation Economics & Management, 7(1), 93–107.

Vukovic, M. (2009). Crowdsourcing for enterprises. 2009 Congress on Services-I, 686–692.

Wazny, K. (2017). “Crowdsourcing” ten years in: A review. Journal of Global Health, 7(2), 20602.

Yuen, M.-C., King, I., & Leung, K.-S. (2011). A survey of crowdsourcing systems. 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, 766–773.

Zhao, Y., & Zhu, Q. (2014). Evaluation on crowdsourcing research: Current status and future direction. Information Systems Frontiers, 16, 417–434.