Peningkatan Kapasitas UMKM Digital di Kota Surabaya melalui Pelatihan AI & Big Data: Dampak, Hambatan, dan Rekomendasi

Fitri; Ari; Burhan

doi:10.63200/jependimas.v2i4.59

Authors

Fitri Komariyah Sekolah Tinggi Ilmu Ekonomi Mahardhika Surabaya, Indonesia
Ari Susanto Sekolah Tinggi Ilmu Ekonomi Mahardhika Surabaya, Indonesia
Burhan Stafrezar Sekolah Tinggi Ilmu Ekonomi Mahardhika Surabaya, Indonesia

DOI:

https://doi.org/10.63200/jependimas.v2i4.59

Keywords:

kapasitas, UMKM, digital, pelatihan, AI, Big Data

Abstract

Perkembangan ekonomi digital menuntut Usaha Mikro, Kecil, dan Menengah (UMKM) untuk beradaptasi dengan teknologi mutakhir, guna meningkatkan daya saing, namun, faktanya masih rendah, karena kesenjangan keterampilan dan sumber daya (OECD, 2021). Tulisan ini bertujuan menganalisis dampak, hambatan, dan merumuskan rekomendasi dari program pelatihan AI dan Big Data yang diimplementasikan untuk UMKM digital di Surabaya. Digunakan pendekatan mixed-methods. Data kuantitatif dikumpulkan melalui survei pra-dan pasca-pelatihan terhadap 50 UMKM peserta, dan data kualitatif diperoleh dari wawancara dan diskusi kelompok (FGD) dengan 15 peserta pelatihan. Fakta menunjukkan pelatihan ini secara signifikan meningkatkan literasi digital dan kapasitas analitis UMKM dari laporan adanya peningkatan kemampuan dalam memanfaatkan data untuk analisis pelanggan, personalisasi pemasaran, dan optimasi operasional (Brynjolfsson & McAfee, 2014). Namun, implementasi pasca-pelatihan mengalami hambatan keterbatasan infrastruktur teknologi dan anggaran, kurangnya sumber daya manusia yang mumpuni; serta resistensi terhadap perubahan dalam budaya organisasi. Program pelatihan AI dan Big Data terbukti efektif sebagai awal pembangunan fondasi digitalisasi UMKM. Rekomendasi yang diusulkan meliputi pembentukan pusat konsultasi teknologi berbiaya rendah bagi UMKM, pengembangan modul pelatihan dan pendampingan, insentif dari Pemerintah Kota bagi UMKM yang berinvestasi dalam teknologi digital.

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References

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage Publications.

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications.

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4. https://doi.org/10.11648/j.ajtas.20160501.11

Hadjimanolis, A., et al. (2021). Digital transformation of SMEs: A review of barriers and enablers. Journal of Small Business Management, 59(3), 1-28. https://doi.org/10.1080/00472778.2021.1883036

Ivankova, N. V., Creswell, J. W., & Stick, S. L. (2006). Using mixed-methods sequential explanatory design: From theory to practice. Field Methods, 18(1), 3-20. https://doi.org/10.1177/1525822X05282260

Kraus, S., et al. (2019). Digital entrepreneurship: A research agenda on new business models for the twenty-first century. Journal of Business Research, 99, 385-398. https://doi.org/10.1016/j.jbusres.2019.02.012

Lee, I. (2018). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303. https://doi.org/10.1016/j.bushor.2018.01.004

Maroufkhani, P., et al. (2020). Artificial intelligence adoption in business-to-business marketing: Toward a conceptual framework. Journal of Business & Industrial Marketing, 35(7), 1121-1132. https://doi.org/10.1108/JBIM-10-2019-0454

Mikalef, P., et al. (2020). Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment. Journal of Business Research, 118, 321-333. https://doi.org/10.1016/j.jbusres.2020.06.026

OECD. (2021). The Digital Transformation of SMEs. OECD Studies on SMEs and Entrepreneurship. OECD Publishing. https://doi.org/10.1787/bdb9256a-en

Scuotto, V., et al. (2020). An alternative way to predict knowledge hiding: The lens of digital transformation. Journal of Business Research, 117, 746-756. https://doi.org/10.1016/j.jbusres.2020.09.006

Tarute, A., et al. (2017). Enhancing dynamic capabilities through digital data and analytics. Journal of Business Research, 70, 226-232. https://doi.org/10.1016/j.jbusres.2016.08.002

Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The processes of technological innovation. Lexington Books.

Ukobitz, F. (2020). AI adoption in German SMEs: Current status, challenges and opportunities. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-28767-1

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2014). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428