Eksplorasi Teknologi Pendukung Data Science untuk Industri Unggulan di Wilayah Kota Surabaya dan Sekitarnya

Authors

  • Rinabi Tanamal Universitas Ciputra Surabaya
  • Yosua Setyawan Soekamto Universitas Ciputra Surabaya
  • Trianggoro Wiradinata Universitas Ciputra Surabaya

DOI:

https://doi.org/10.32493/informatika.v6i2.10056

Keywords:

data science, pattern recognition, clustering, prediction, decision making

Abstract

Data science competence is still quite uncommon for most people, even though this competence is very important in managing business development strategies in various leading industrial fields. Through data science, companies can find patterns, perform clustering, calculate predictions more accurately to make decisions that have an impact on company excellence. Lack of knowledge about data science, researchers seek firstly in the industry to explore tools that are mostly used, convenience, challenges, measuring success tools and advantages. In this study, the researchers conducted a Focus Group Discussion by interviewing resource persons who worked as Data Scientists. Interview method to discuss aspects of Demographics, aspects of Technology-Task Fitness, and aspects of the Technology Acceptance Model. The results of this study conclude that the tools that are the mainstay of the industry in Surabaya and its surroundings area are Jupyter Notebook, Python, and Tableau. The success in using these tools still depends on the user's abilities, the results of user simulations, team discussions, and the evaluation process.

References

Ahmed, T., Chandran, V.G.R., Klobas, J.E., Liñán, F. and Kokkalis, P. (2020) ‘Entrepreneurship education programmes: how learning, inspiration and resources affect intentions for new venture creation in a developing economy’, The International Journal of Management Education, Vol. 18, No. 1, p.100327.

Berman, F., Rutenbar, R., Hailpern, B., Christensen, H., Davidson, S., Estrin, D., Franklin, M., Martonosi, M., Raghavan, P., Stodden, V., & Szalay, A. S. (2018, April). Realizing the Potential of Data Science. Communications of the ACM, 61(04), 67–72. https://doi.org/10.1145/3188721

Bischoff, K., Volkmann, C. K., & Audretsch, D. B. (2018). Stakeholder collaboration in entrepreneurship education: An analysis of the entrepreneurial ecosystems of European higher educational institutions. The Journal of Technology Transfer, 43(1), 20–46.

Liñán, F., Rodríguez-Cohard, J. C., & Rueda-Cantuche, J. M. (2011). Factors affecting entrepreneurial intention levels: A role for education. The International Entrepreneurship and Management Journal, 7(2), 195–218.

Medeiros, M. M. de, Hoppen, N., & Maçada, A. C. G. (2020). Data science for business: benefits, challenges and opportunities. Bottom Line, 33(2), 149–163. https://doi.org/10.1108/BL-12-2019-0132

Nabi, G., Walmsley, A., Liñán, F., Akhtar, I., & Neame, C. (2018). Does entrepreneurship education in the first year of higher education develop entrepreneurial intentions? The role of learning and inspiration. Studies in Higher Education, 43(3), 452–467.

O. Nyumba, T., Wilson, K., Derrick, C. J., & Mukherjee, N. (2018). The use of focus group discussion methodology: Insights from two decades of application in conservation. Methods in Ecology and evolution, 9(1), 20-32.

Rogala, P., Batko, R., & Wawak, S. (2017). Factors affecting success of training companies. Studies in Continuing Education, 39(3), 357–370. https://doi.org/10.1080/0158037X.2017.1336995

Tanamal, R. (2019). What Is the Most Influential Factor On Decisions Using Youtube As A Tool To Support Buy Or Sell Means? Case Study Surabaya City And Surrounding Area. Journal of Theoretical and Applied Information Technology, 97(20), 2406-2418.

Wiradinata, T., & Antonio, T. (2018). Developing Technology Entrepreneurship Subjects: A Four-Year Evaluation. International Journal of Innovation, Management and Technology, 9(1), 37–41. https://doi.org/10.18178/ijimt.2018.9.1.784.

Wu, Y. C. J., Kuo, T., & Shen, J. P. (2013). Exploring social entrepreneurship education from a web-based pedagogical perspective. Computers in Human Behavior, 29(2), 329–334

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Published

2021-06-30