Memanfaatkan AI/ML untuk Mengoptimalkan Pengambilan Keputusan di Era Ekonomi Digital
Abstract
Di era digital yang dinamis, pengambilan keputusan yang cepat, akurat, dan berbasis data menjadi kunci bagi perusahaan dan organisasi untuk bersaing. Pengambilan keputusan tradisional tidak lagi memadai untuk menghadapi kompleksitas dan perubahan yang cepat. Artificial Intelligance (AI) / Mechine Learning (ML) menawarkan solusi yang menjanjikan untuk mengoptimalkan proses pengambilan keputusan.
Penelitian ini bertujuan untuk menganalisis potensi AI/ML dalam mengoptimalkan pengambilan keputusan di era ekonomi digital Indonesia. Penelitian ini mengidentifikasi berbagai use case penerapan AI/ML di berbagai bidang, seperti perumusan kebijakan dan manajemen sumber daya manusia. Manfaat dan tantangan penerapan AI/ML dalam pengambilan keputusan juga dibahas.
Hasil penelitian menunjukkan bahwa AI/ML memiliki potensi besar untuk meningkatkan kualitas pengambilan keputusan dengan menganalisis data dalam skala besar, mengidentifikasi pola, dan memberikan wawasan yang berharga. Penerapan AI/ML dapat meningkatkan efisiensi operasional, mengurangi risiko, dan mempersonalisasi layanan. Penerapan AI/ML yang tepat dapat membantu perusahaan dan organisasi untuk mencapai tujuan strategis dan meningkatkan daya saing perusahaan
References
[2] McKinsey. The next normal arrives: Trends that will define 2021—and beyond. McKinsey & Company. 2020.
[3] Harvard Business Review. The Fading Allure of the Intuitive Decision Maker. Harvard Business Review. 2019.
[4] Deloitte. Accelerating agility with everything-as-a-service. Deloitte Insights. 2018
[5] PwC. Sizing the prize: What's the real value of AI for your business and how can you capitalise? PwC. 2017.
[6] MIT Sloan Management Review. Leading With Next-Generation Key Performance Indicators. MIT Sloan Management Review. 2018.
[7] Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. A future that works: Automation, employment, and productivity. McKinsey Global Institute. 2017
[8] Russell, S. J., & Norvig, P. Artificial Intelligence: A Modern Approach. Pearson Education. 2016.
[9] Alpaydin, E. Introduction to Machine Learning. MIT Press. 2020.
[10] Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning. MIT Press. 2016.
[11] Kahneman, D., Sibony, O., & Sunstein, C. R. Noise: A Flaw in Human Judgment. Little, Brown Spark. 2021.
[12] Bishop, C. M. Pattern Recognition and Machine Learning. Springer. 2016.
[13] Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. Artificial Intelligence and the 'Good Society': the US, EU, and UK approach. Science and Engineering Ethics, 24(2).2018: 505-528.
[14] Agrawal, A., Gans, J., & Goldfarb, A. Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press. 2018.
[15] Khosrow-Pour, M. Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global. 2017.
[16] Rajaraman, A., & Ullman, J. D. (2011). Mining of Massive Datasets. Cambridge University Press. 2011.
[17] Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4). 2018: 689-707.
[18] Jobin, A., Ienca, M., & Vayena, E. The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9). 2019: 389-399.
[19] Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.2016.
[20] Dignum, V. Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer. 2019.
[21] Yin, R. K. Case study research and applications: Design and methods. Sage publications. 2018
[22] Creswell, J. W., & Poth, C. N. Qualitative inquiry and research design: Choosing among five approaches. Sage publications. 2018
[23] Braun, V., & Clarke, V. Using thematic analysis in psychology. Qualitative research in psychology, 3(2). 2006: 77-101.
[24] Saldaña, J. (2015). The coding manual for qualitative researchers. Sage
[25] Davenport, T. H., & Ronanki, R. Artificial intelligence for the real world. Harvard Business Review, 96(1). 2018: 108-116.
[26] Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. Reshaping business with artificial intelligence. MIT Sloan Management Review, 59(1). 2017
[27] Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. A future that works: Automation, employment, and productivity. McKinsey Global Institute. 2017.
[28] Wang, W., & Benbasat, I. Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems, 23(4). 2007: 217-246
[29] Baesens, B., Backiel, A., & Mulders, M. Analytics in a big data world: The essential guide to data science and its applications. John Wiley & Sons. 2016
[30] Lee, J., Bagheri, B., & Kao, H. A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3. 2018:18-23.
[31] Davenport, T. H., & Kalakota, R. The potential for artificial intelligence in healthcare. Future healthcare journal. 2019. 2019 ; 6(2): 94.
[32] Verhoef, P. C., Kannan, P. K., & Inman, J. J. From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. Journal of retailing. 2015 ; 91(2) : 174-181
[33] Brynjolfsson, E., & McAfee, A. The business of artificial intelligence. Harvard Business Review. 2017; 25, 3-11.
[34] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR). 2021; 54(6) : 1-35.
[35] Davenport, T. H. The AI advantage: How to put the artificial intelligence revolution to work. MIT Press.2018.
Copyright (c) 2024 I Putu Putra Astawa; Ida Ayu Utari Dewi
This work is licensed under a Creative Commons Attribution 4.0 International License.
