Literature Review Penggunaan Teknologi Kecerdasan Buatan dalam Penerbangan: Analisis Perkembangan Teknologi, Potensi Keamanan, dan Tantangan
DOI:
https://doi.org/10.31933/jimt.v4i5.1484Keywords:
Artificial Intelligence, Aviation, Security Potential, ChallengesAbstract
This literature review examines the development and potential of Artificial Intelligence (AI) in aviation, particularly in aircraft design, air traffic control, and maintenance. AI has the potential to revolutionize the industry by making aircrafts more efficient, lighter, and safer. The use of AI in air traffic control can also reduce delays and improve safety. One of the most significant potential benefits of AI in aviation is increased security, as it can be used to detect potential security threats and prevent hijackings or other security breaches. However, the implementation of AI in aviation also presents challenges such as significant investment in infrastructure and training, concerns about job loss, and ensuring the security and privacy of data. Despite these challenges, the potential benefits of AI in aviation make it a promising field of development for the industry.
References
Andrade, P. et al. (2021) ‘Aircraft Maintenance Check Scheduling Using Reinforcement Learning’, Aerospace, 8(113), pp. 1–18.
Borhani, M. et al. (2020) ‘A Multicriteria Optimization for Flight Route Networks in Large-Scale Airlines Using Intelligent Spatial Information’, International Journal of Interactive Multimedia and Artificial Intelligence, 6(1), p. 123. doi: 10.9781/ijimai.2019.11.001.
Degas, A. et al. (2022) ‘A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory’, Applied Sciences (Switzerland), 12(3), pp. 1–47. doi: 10.3390/app12031295.
Gupta, A. and Kumar, A. (2021) ‘Artificial Intelligence in Aviation’, Journal of Aeronautics & Aerospace, 10(10), pp. 1–7.
Hermes (2020) The Flight to Safety-Critical AI. Maryland.
La, J., Bil, C. and Heiets, I. (2020) ‘Impact of digital technologies on airline operations’, IFAC-PapersOnLine, 56(C), pp. 63–70. doi: 10.1016/j.trpro.2021.09.008.
Lee, P. S., Chakraborty, I. and Banerjee, S. (2022) ‘AI Applications to Customer Feedback Research?: A Review’, SSRN, pp. 1–27.
Ortner, P. et al. (2022) ‘Augmented Air Traffic Control System—Artificial Intelligence as Digital Assistance System to Predict Air Traffic Conflicts’, Ai, 3(3), pp. 623–644. doi: 10.3390/ai3030036.
Rajapathirana, R. P. J. and Hui, Y. (2018) ‘Relationship between innovation capability, innovation type, and firm performance’, Journal of Innovation and Knowledge, 3(1), pp. 44–55. doi: 10.1016/j.jik.2017.06.002.
Ramli, A. A. et al. (2014) ‘A Practical Weather Forecasting for Air Traffic Control System using Fuzzy Hierarchical Technique’, Advances in Intelligent Systems and Computing, 287(January). doi: 10.1007/978-3-319-07692-8.
Ridha, M. (2022) ‘Implementation of Artificial Intelligence Chatbot in Optimizing Customer Service in Financial Technology Company PT . FinAccel Finance Indonesia’, Proceedings, 83(21).
SESAR Joint Undertaking (2018) Artificial intelligence in air traffic management. Brussels: SESAR Joint Undertaking. Available at: https://www.sesarju.eu/node/3024.
Swischuk, R. and Allaire, D. (2018) ‘A machine learning approach to aircraft sensor error detection and correction’, American Institute of Aeronautics and Astronautics, (November). doi: 10.2514/6.2018-1164.
Taleqani, A. R. and Bridgelall, R. (2018) ‘Machine Learning Approach to Cyber Security in Aviation’, in 2018 IEEE International Conference on Electro/Information Technology (EIT). doi: 10.1109/EIT.2018.8500165.
Tang, J., Liu, G. and Pan, Q. (2022) ‘Review on artificial intelligence techniques for improving representative air traffic management capability’, Journal of Systems Engineering and Electronics, 33(5), pp. 1123–1134. doi: 10.23919/JSEE.2022.000109.
Wang, L. et al. (2021) ‘Exploring the impact of regulatory environment on airline innovation performance: An empirical analysis’, Journal of Air Transport Management, 91.
Xin, O. K., Wider, W. and Ling, L. K. (2022) ‘Human Resource Artificial Intelligence Implementation and Organizational Performance in Malaysia’, Asia-Pacific Social Science Review, 22(3), pp. 18–37.
Borhani, M. et al. (2020) ‘A Multicriteria Optimization for Flight Route Networks in Large-Scale Airlines Using Intelligent Spatial Information’, International Journal of Interactive Multimedia and Artificial Intelligence, 6(1), p. 123. doi: 10.9781/ijimai.2019.11.001.
Degas, A. et al. (2022) ‘A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory’, Applied Sciences (Switzerland), 12(3), pp. 1–47. doi: 10.3390/app12031295.
Gupta, A. and Kumar, A. (2021) ‘Artificial Intelligence in Aviation’, Journal of Aeronautics & Aerospace, 10(10), pp. 1–7.
Hermes (2020) The Flight to Safety-Critical AI. Maryland.
La, J., Bil, C. and Heiets, I. (2020) ‘Impact of digital technologies on airline operations’, IFAC-PapersOnLine, 56(C), pp. 63–70. doi: 10.1016/j.trpro.2021.09.008.
Lee, P. S., Chakraborty, I. and Banerjee, S. (2022) ‘AI Applications to Customer Feedback Research?: A Review’, SSRN, pp. 1–27.
Ortner, P. et al. (2022) ‘Augmented Air Traffic Control System—Artificial Intelligence as Digital Assistance System to Predict Air Traffic Conflicts’, Ai, 3(3), pp. 623–644. doi: 10.3390/ai3030036.
Rajapathirana, R. P. J. and Hui, Y. (2018) ‘Relationship between innovation capability, innovation type, and firm performance’, Journal of Innovation and Knowledge, 3(1), pp. 44–55. doi: 10.1016/j.jik.2017.06.002.
Ramli, A. A. et al. (2014) ‘A Practical Weather Forecasting for Air Traffic Control System using Fuzzy Hierarchical Technique’, Advances in Intelligent Systems and Computing, 287(January). doi: 10.1007/978-3-319-07692-8.
Ridha, M. (2022) ‘Implementation of Artificial Intelligence Chatbot in Optimizing Customer Service in Financial Technology Company PT . FinAccel Finance Indonesia’, Proceedings, 83(21).
SESAR Joint Undertaking (2018) Artificial intelligence in air traffic management. Brussels: SESAR Joint Undertaking. Available at: https://www.sesarju.eu/node/3024.
Swischuk, R. and Allaire, D. (2018) ‘A machine learning approach to aircraft sensor error detection and correction’, American Institute of Aeronautics and Astronautics, (November). doi: 10.2514/6.2018-1164.
Taleqani, A. R. and Bridgelall, R. (2018) ‘Machine Learning Approach to Cyber Security in Aviation’, in 2018 IEEE International Conference on Electro/Information Technology (EIT). doi: 10.1109/EIT.2018.8500165.
Tang, J., Liu, G. and Pan, Q. (2022) ‘Review on artificial intelligence techniques for improving representative air traffic management capability’, Journal of Systems Engineering and Electronics, 33(5), pp. 1123–1134. doi: 10.23919/JSEE.2022.000109.
Wang, L. et al. (2021) ‘Exploring the impact of regulatory environment on airline innovation performance: An empirical analysis’, Journal of Air Transport Management, 91.
Xin, O. K., Wider, W. and Ling, L. K. (2022) ‘Human Resource Artificial Intelligence Implementation and Organizational Performance in Malaysia’, Asia-Pacific Social Science Review, 22(3), pp. 18–37.
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Published
2023-06-12
How to Cite
Biringkanae, P., & Bunahri, R. R. (2023). Literature Review Penggunaan Teknologi Kecerdasan Buatan dalam Penerbangan: Analisis Perkembangan Teknologi, Potensi Keamanan, dan Tantangan. Jurnal Ilmu Manajemen Terapan, 4(5), 745–752. https://doi.org/10.31933/jimt.v4i5.1484
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