Peran Kecerdasan Buatan dalam Sistem Pertahanan Udara untuk Pengambilan Keputusan Taktis

Muhammad Ismed Nafriza, Erry Herman, Cahyadi Amperawan

Abstract


Kecerdasan buatan (AI) telah mentransformasi sistem pertahanan udara, meningkatkan efisiensi dan akurasi dalam pengambilan keputusan taktis. Penelitian ini bertujuan mengembangkan sistem AI adaptif dan kolaboratif untuk meningkatkan pengambilan keputusan taktis dalam pertahanan udara. Menggunakan metode studi literatur deskriptif kualitatif, penelitian ini menganalisis berbagai sumber literatur terkait penerapan AI dalam sistem pertahanan udara. Hasil menunjukkan bahwa integrasi AI meningkatkan kesadaran situasional, efisiensi operasional, dan kemampuan perencanaan strategis. Metode seperti deep reinforcement learning dan teori permainan terbukti efektif dalam mengatasi kompleksitas pertempuran udara, terutama dalam skenario yang melibatkan Unmanned Combat Aerial Vehicles (UCAVs) dan sistem multi-agen. Namun, tantangan seperti keterbatasan waktu pengambilan keputusan, kebutuhan transparansi, dan pertimbangan etika masih perlu diatasi. Penelitian juga mengidentifikasi pentingnya pengembangan AI yang dapat dijelaskan (Explainable AI) untuk meningkatkan kepercayaan terhadap keputusan AI dalam konteks militer. Kesimpulannya, meskipun AI menawarkan potensi besar, diperlukan pengembangan lebih lanjut dalam algoritma hybrid, XAI, dan model kolaborasi manusia-mesin untuk memaksimalkan efektivitas dan keandalan sistem pertahanan udara.

Kata kunci: Kecerdasan Buatan, Pertahanan Udara, Pengambilan Keputusan Taktis, Pembelajaran Mendalam, Reinforcement Learning


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Azis, N. A., Widyotriatmo, A., Sumari, A. D., Juliastuti, E., & Nazaruddin, Y. Y. (2019). Dynamic High-Performance Decision-Making in Air Defense System Operation. 2019 IEEE 6th Asian Conference on Defence Technology (ACDT). https://doi.org/10.1109/ACDT47198.2019.9072897

Borisova, A. R., & Nikolov, L. (2023). SYSTEMS WITH ARTIFICIAL INTELLIGENCE FOR DEFENSE AND SECURITY. SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE. https://doi.org/10.19062/2247-3173.2023.24.7

Chao, L., Jiafan, H., Liqingwei, W., & Man, W. (2020). An Air Combat Simulation System for Intelligent Decision-Making. 2020 12th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). https://doi.org/10.1109/IHMSC49165.2020.10102

Creswell, J. W., & Creswell, J. David. (2018). Research Design : Qualitative, Quantitative, and Mixed Methods Approaches. In SAGE Publications, Inc. (US) (5th ed.). SAGE.

Dantas, J. P. A., Maximo, M., Costa, A. N., Geraldo, D., & Yoneyama, T. (2022). Machine Learning to Improve Situational Awareness in Beyond Visual Range Air Combat. IEEE Latin America Transactions, 20(8), 2039-2045. https://doi.org/10.1109/TLA.2022.9853232

Das, S. (2014). Modeling Intelligent Decision-Making Command and Control Agents: An Application to Air Defense. IEEE Intelligent Systems, 29(2), 22-29. https://doi.org/10.1109/MIS.2013.71

Davis, S. (2022). Artificial intelligence at the operational level of war. Defense & Security Analysis, 38(2), 74-90. https://doi.org/10.1080/14751798.2022.2031692

Feldman, P. G., Dant, A., & Massey, A. K. (2019). Integrating Artificial Intelligence into Weapon Systems. ArXiv. https://doi.org/10.48550/arXiv.1905.03899

Fu, Q., Fan, C., Song, Y., & Guo, X. (2020). Alpha C2–An Intelligent Air Defense Commander Independent of Human Decision-Making. IEEE Access, 8, 87504-87516. https://doi.org/10.1109/ACCESS.2020.2993459

Jiang, X., Wu, W., Liu, T., & Hao, L. (2022). Research on the Intelligent Air-defense Command and Decision System Based on Multi-agent. 2022 2nd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS). https://doi.org/10.1109/acctcs53867.2022.00034

Jin, X., Wang, X., & Cai, F. (2021). Context Aware Combat COA Recommendation using Preference Learning. Journal of Physics: Conference Series, 1993. https://doi.org/10.1088/1742-6596/1993/1/012037.

John, W., & Kenneth, H. (1989). Application of Artificial Intelligence (AI) Programming Techniques to Tactical Guidance For Fighter Aircraft. https://doi.org/10.2514/6.1989-3525

Johnson, B., & Treadway, W. A. (2019). Artificial Intelligence - An Enabler of Naval Tactical Decision Superiority. AI Mag., 40(1), 63-78. https://doi.org/10.1609/AIMAG.V40I1.2852

Khan, I. A., Imam, I., & Azam, A. (2021). Role of Artificial Intelligence in Defence Strategy: Implications for Global and National Security. Strategic Studies. https://doi.org/10.53532/ss.041.01.0058

Li, Y., Han, W., & Wang, Y. (2020). Deep Reinforcement Learning With Application to Air Confrontation Intelligent Decision-Making of Manned/Unmanned Aerial Vehicle Cooperative System. IEEE Access, 8, 67887-67898. https://doi.org/10.1109/ACCESS.2020.2985576.

Li, Y., Qiu, X., Liu, X., & Xia, Q. (2020). Deep reinforcement learning and its application in autonomous fitting optimization for attack areas of UCAVs. Journal of Systems Engineering and Electronics. https://doi.org/10.23919/jsee.2020.000048.

Lirov, Y., Rodin, E., & Ghosh, B. (1989). Automated learning by tactical decision systems in air combat. Computers & Mathematics With Applications, 18, 151-160. https://doi.org/10.1016/0898-1221(89)90132-6

Liu, P., & Ma, Y. (2017). A Deep Reinforcement Learning Based Intelligent Decision Method for UCAV Air Combat. , 274-286. https://doi.org/10.1007/978-981-10-6463-0_24.

Maathuis, C. (2022). On Explainable AI Solutions for Targeting in Cyber Military Operations. International Conference on Cyber Warfare and Security. https://doi.org/10.34190/iccws.17.1.38.

McManus, J. W. (1990). A concurrent distributed system for aircraft tactical decision generation. 9th IEEE/AIAA/NASA Conference on Digital Avionics Systems. https://doi.org/10.1109/DASC.1990.111340

Rawat, D. B. (2022). Artificial Intelligence Meets Tactical Autonomy: Challenges and Perspectives. 2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI), 49-51. https://doi.org/10.1109/CogMI56440.2022.00017

Rodin, E., Lirov, Y., Mittnik, S., McElhaney, B., & Wilbur, L. (1987). Artificial intelligence in air combat games. Computers & Mathematics With Applications, 13(3-4), 261-274. https://doi.org/10.1016/0898-1221(87)90109-X

Schraagen, J. (2023). Responsible use of AI in military systems: Prospects and challenges.. Ergonomics, 1-16 . https://doi.org/10.1080/00140139.2023.2278394.

Sugiyono. (2019). Metodelogi Penelitian Kuantitatif dan Kualitatif (2nd ed.). Alfabeta.

Wang, Z., Li, H., Wu, H., & Wu, Z. (2020). Improving Maneuver Strategy in Air Combat by Alternate Freeze Games with a Deep Reinforcement Learning Algorithm. Mathematical Problems in Engineering, 2020, 1-17. https://doi.org/10.1155/2020/7180639.

Wu, Y., Lei, Y., Zhu, Z., & Wang, Y. (2022). Decision Modeling and Simulation of Fighter Air-to-ground Combat Based on Reinforcement Learning. Proceedings of the 4th International Conference on Image Processing and Machine Vision. https://doi.org/10.1145/3529446

Yi, W. (2003). An Intelligent Differential Game on Air Combat Decision. Flight Dynamics. https://doi.org/10.1007/s10846-009-9316-4

Zhang, G., Li, Y., Xu, X., & Dai, H. (2019). Efficient Training Techniques for Multi-Agent Reinforcement Learning in Combat Tasks. IEEE Access, 7, 109301-109310. https://doi.org/10.1109/ACCESS.2019.2933454.




DOI: https://doi.org/10.31604/jim.v8i4.2024.%25p

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.