Published 2025-12-29
Keywords
- AI generatif,
- manajemen risiko,
- proyek aplikasi mobile,
- informatika,
- Generative AI
- risk management,
- mobile application projects,
- hybrid approach,
- informatics ...More
How to Cite
Abstract
Pengembangan aplikasi mobile menghadapi risiko kompleks seperti keterlambatan jadwal, overrun anggaran, dan kegagalan teknis, yang diperburuk oleh dinamika pasar digital yang cepat. Artikel ini mengeksplorasi integrasi AI generatif, seperti model berbasis GPT, dalam manajemen risiko proyek pengembangan aplikasi mobile melalui pendekatan hybrid yang menggabungkan kemampuan AI dengan intervensi manusia. Tujuan penelitian adalah untuk mengembangkan kerangka kerja yang meningkatkan efisiensi identifikasi, analisis, dan mitigasi risiko, sambil meminimalkan bias manusia dan memaksimalkan inovasi. Metode penelitian melibatkan studi kasus pada tiga proyek aplikasi mobile di Indonesia, menggunakan analisis kualitatif dan kuantitatif dengan alat AI untuk simulasi risiko dan validasi oleh tim proyek. Hasil menunjukkan bahwa AI generatif dapat mengurangi waktu identifikasi risiko hingga 40% dan meningkatkan akurasi prediksi hingga 25%, namun efektivitasnya bergantung pada kolaborasi manusia untuk menangani konteks etis dan kreatif. Implikasi praktis mencakup rekomendasi bagi manajer proyek informatika untuk mengadopsi pendekatan ini di era digital 2024, dengan potensi peningkatan keberhasilan proyek. Penelitian ini berkontribusi pada literatur manajemen proyek IT dengan menekankan keseimbangan antara teknologi AI dan kecerdasan manusia, yang belum banyak dieksplorasi secara spesifik.
Mobile application development faces complex risks such as schedule delays, budget overruns, and technical failures, exacerbated by the rapid dynamics of the digital market. This article explores the integration of generative AI, such as GPT-based models, in risk management of mobile application development projects through a hybrid approach that combines AI capabilities with human intervention. The research objective is to develop a framework that improves the efficiency of risk identification, analysis, and mitigation, while minimizing human bias and maximizing innovation. The research methods include case studies of three mobile application projects in Indonesia, using qualitative and quantitative analysis with AI tools for risk simulation and validation by the project team. The results show that generative AI can reduce risk mitigation time by up to 40% and improve prediction accuracy by up to 25%. However, its effectiveness relies on human collaboration to address ethical and creative contexts. Practical implications include recommendations for information technology project managers to adopt this approach in the digital era of 2024, with the potential to increase project success. This research contributes to the IT project management literature, addressing the tension between AI technology and human intelligence, a topic that has not been widely explored.
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References
- [1] Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., ... & Amodei, D. (2020). “Language models are few-shot learners”. Advances in Neural Information Processing Systems, 33, 1877–1901. Available : https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
- [2] Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Velu, A., Wong, J., Yee, L., & Zhang, Z. (2023). The state of AI in 2023: Generative AI’s breakout year. McKinsey & Company. Available: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year
- [3] Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.
- [4] Dumas, J. S., & Redish, J. C. (1999). A practical guide to usability testing. Intellect Books.
- [5] Floridi, L. (2020). AI ethics. In M. Dubber, F. Pasquale, & S. Das (Eds.), The Oxford handbook of ethics of AI (pp. 1–22). Oxford University Press. Available: https://doi.org/10.1093/oxfordhb/9780190067397.013.1
- [6] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
- [7] Kerzner, H. (2017). Project management: A systems approach to planning, scheduling, and controlling (12th ed.). Wiley.
- [8] Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK® guide) (7th ed.). Project Management Institute.
- [9] Project Management Institute. (2022). Pulse of the profession: The state of project management 2022. Project Management Institute. Available: https://www.pmi.org/learning/thought-leadership/pulse
- [10] Smith, J. (2021). “AI in risk management for IT projects”. Journal of Information Technology Management, 32(1), 45–60. Available: https://doi.org/10.1080/10580530.2021.1891234