Pemetaan Elemen Artificial Intelligence Sesuai Tiga Struktur Pertahanan: Operational, Tactical, Dan Strategic

Katherine Erika; Asep Adang Supriyadi; Ghazalie Ghazalie; Alradix Djansena

doi:10.55681/jige.v6i4.4833

Authors

Katherine Erika Republic of Indonesia Defense University, Indonesia
Asep Adang Supriyadi Republic of Indonesia Defense University, Indonesia
Ghazalie Republic of Indonesia Defense University, Indonesia
Alradix Djansena Republic of Indonesia Defense University, Indonesia

DOI:

https://doi.org/10.55681/jige.v6i4.4833

Keywords:

Artificial Intelligence, Defense Policy, Machine Learning, Neural Networks, Natural Language Processing, Robotics, Cyber Defense

Abstract

This research analyzes the AI components Machine Learning, Neural Networks, Natural Language Processing, and Robotics and their allocation within Indonesia’s three-tiered defense structure: operational, tactical, and strategic. This paper explores the integration of Artificial Intelligence (AI) into Indonesia's national defense policy, examining opportunities, challenges, and strategic implications. AI's transformative potential spans operational efficiency, strategic decision-making, and robust cyber security. This research employs a qualitative approach, examining previously published scholarly articles across various journals to understand the multifaceted dimensions of Artificial Intelligence. Key challenges are identified: infrastructure requirements, dependence on foreign technology, ethical concerns including data bias, and transparency in AI-driven decisions. A comprehensive policy framework is proposed, emphasizing strategic partnerships, robust data governance, and ethical guidelines to mitigate risks and maximize AI’s benefits. The discussion highlights the crucial need for AI-specific hardware investment, including microchips and supercomputing infrastructure, while advocating for fostering local expertise and reducing reliance on external providers. The paper argues that AI empowers Indonesia to enhance military capabilities, strengthen cyber defenses, and optimize strategic decision-making. However, a balanced approach that prioritizes ethical considerations, transparency, and a clearly defined command chain is crucial for responsible AI deployment. This research serves as a roadmap for Indonesian policymakers to navigate the complex landscape of AI in national defense, ensuring sovereignty, security, and ethical alignment in the digital age. The implementation of AI is not just an option but a strategic imperative.

Downloads

Download data is not yet available.

References

Abderrahmane, N., Lemaire, E., & Miramond, B. (2020). Design space exploration of hardware spiking neurons for embedded artificial intelligence. Neural Networks, 121, 366-386.

Bahado-Singh, R. O., Radhakrishna, U., Gordevičius, J., Aydas, B., Yilmaz, A., Jafar, F., ... & Vishweswaraiah, S. (2022). Artificial intelligence and circulating cell-free DNA methylation profiling: Mechanism and detection of Alzheimer’s disease. Cells, 11(11), 1744.

Bai, B., Shu, H., Wang, X., & Zou, W. (2020). Towards silicon photonic neural networks for artificial intelligence. Science China Information Sciences, 63, 1-14.

Baptista, D., Abreu, S., Freitas, F., Vasconcelos, R., & Morgado-Dias, F. (2013). A survey of software and hardware use in artificial neural networks. Neural Computing and Applications, 23, 591-599.

Berggren, K., Xia, Q., Likharev, K. K., Strukov, D. B., Jiang, H., Mikolajick, T., ... & Raychowdhury, A. (2020). Roadmap on emerging hardware and technology for machine learning. Nanotechnology, 32(1), 012002.

Boybat, I., Payvand, M., Rhodes, O., & Serb, A. (2022). Hardware for artificial intelligence. Frontiers in Neuroscience, 16, 979495.

Conroy, B., Silva, I., Mehraei, G., Damiano, R., Gross, B., Salvati, E., ... & McFarlane, D. C. (2022). Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19. Scientific reports, 12(1), 3797.

Courtland, R. (2018). The bias detectives. Nature, 558(7710), 357-360.

De Filippo, A., Borghesi, A., Boscarino, A., & Milano, M. (2022). HADA: An automated tool for hardware dimensioning of AI applications. Knowledge-Based Systems, 251, 109199.

Deng, C., Fang, X., Wang, X., & Law, K. (2022). Software orchestrated and hardware accelerated artificial intelligence: Toward low latency edge computing. IEEE Wireless Communications, 29(4), 110-117.

Dias, F. M., Antunes, A., & Mota, A. M. (2004). Artificial neural networks: a review of commercial hardware. Engineering Applications of Artificial Intelligence, 17(8), 945-952.

Galán, J. J., Carrasco, R. A., & LaTorre, A. (2022). Military applications of machine learning: A bibliometric perspective. Mathematics, 10(9), 1397.

Gjesvik, L., & Willers, J. O. (2024). Beyond control? The political economy of private interception, intrusion, and surveillance markets. Review of International Political Economy, 31(6), 1840-1864.

Gupta, N. (2021). Introduction to hardware accelerator systems for artificial intelligence and machine learning. In Advances in Computers (Vol. 122, pp. 1-21). Elsevier.

Gutierrez, M. I., Ramos, A., Gutierrez, J., Vera, A., & Leija, L. (2019). Nonuniform Bessel-Based Radiation Distributions on A Spherically Curved Boundary for Modeling the Acoustic Field of Focused Ultrasound Transducers. Applied Sciences, 9(5), 911. https://doi.org/10.3390/app9050911

Hadlington, L., Binder, J., Gardner, S., Karanika-Murray, M., & Knight, S. (2023). The use of artificial intelligence in a military context: development of the attitudes toward AI in defense (AAID) scale. Frontiers in Psychology, 14, 1164810.

Jia, Y., Gu, Z., Du, L., Long, Y., Wang, Y., Li, J., & Zhang, Y. (2023). Artificial intelligence enabled cyber security defense for smart cities: A novel attack detection framework based on the MDATA model. Knowledge-Based Systems, 276, 110781.

Johnson, B. (2021). Artificial intelligence systems: unique challenges for defense applications. Acquisition Research Program.

Johnson, B., & Treadway, W. A. (2019). Artificial intelligence—an enabler of naval tactical decision superiority. Ai Magazine, 40(1), 63-78.

Johnson, W. L., & Valente, A. (2009). Tactical language and culture training systems: Using AI to teach foreign languages and cultures. AI magazine, 30(2), 72-72.

Khalaf, B. A., Mostafa, S. A., Mustapha, A., Mohammed, M. A., & Abduallah, W. M. (2019). Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defense methods. IEEE Access, 7, 51691-51713. https://doi.org/10.1109/ACCESS.2019.2908998

Kim, S., & Deka, G. C. (2021). Hardware accelerator systems for artificial intelligence and machine learning (Vol. 122). Academic Press.

Lee, D., & Lee, S. T. (2023). Artificial intelligence enabled energy-efficient heating, ventilation and air conditioning system: Design, analysis and necessary hardware upgrades. Applied Thermal Engineering, 235, 121253.

Lee, K. J., Lee, J., Choi, S., & Yoo, H. J. (2020). The development of silicon for AI: Different design approaches. IEEE Transactions on Circuits and Systems I: Regular Papers, 67(12), 4719-4732.

Meerveld, H. W., Lindelauf, R. H. A., Postma, E. O., & Postma, M. (2023). The irresponsibility of not using AI in the military. Ethics and Information Technology, 25(1), 14.

Mori, S. (2018). US Defense Innovation and Artificial Intelligence. Asia-Pacific Review, 25(2), 16–44. https://doi.org/10.1080/13439006.2018.1545488

Murmu, S., Sinha, D., Chaurasia, H., Sharma, S., Das, R., Jha, G. K., & Archak, S. (2024). A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions. Frontiers in Plant Science, 15, 1292054.

Park, H., & Kim, S. (2021). Hardware accelerator systems for artificial intelligence and machine learning. In Advances in Computers (Vol. 122, pp. 51-95). Elsevier.

Tai, X. Y., Zhang, H., Niu, Z., Christie, S. D., & Xuan, J. (2020). The future of sustainable chemistry and process: Convergence of artificial intelligence, data and hardware. Energy and AI, 2, 100036.

Park, J. M., & Lee, B. G. (2020). Analysis of Research and Development Efficiency of Artificial Intelligence Hardware of Global Companies using Patent Data and Financial data. Journal of Korea Multimedia Society, 23(2), 317-327.

Park, S., Moura, S., & Lee, K. (2023). Integration of hardware and software for battery hardware-in-the-loop toward battery artificial intelligence. IEEE Transactions on Transportation Electrification, 10(1), 888-900.

Pomerol, J. C. (1997). Artificial intelligence and human decision making. European Journal of Operational Research, 99(1), 3-25.

Rasch, R., Kott, A., & Forbus, K. D. (2003). Incorporating AI into military decision making: an experiment. IEEE Intelligent Systems, 18(4), 18-26.

Rashid, A. B., Kausik, A. K., Al Hassan Sunny, A., & Bappy, M. H. (2023). Artificial intelligence in the military: An overview of the capabilities, applications, and challenges. International journal of intelligent systems, 2023(1), 8676366.

Reinisch, F., Strohal, M., & Stütz, P. (2023, October). A Tactical Planning Process in Computer-Generated Forces Team Behavior Within Air Combat Simulations: Concept and First Implementations. In International Conference on Modelling and Simulation for Autonomous Systems (pp. 247-262). Cham: Springer Nature Switzerland.

Reis, J., Cohen, Y., Melão, N., Costa, J., & Jorge, D. (2021). High-tech defense industries: developing autonomous intelligent systems. Applied Sciences, 11(11), 4920.

Roccetti, M., Tenace, M., & Cappiello, G. (2024, September). Prescient Perspectives on Football Tactics: A Case with Liverpool FC, Corners and AI. In International Conference on Advances in Social Networks Analysis and Mining (pp. 231-239). Cham: Springer Nature Switzerland.

Saldiran, E., Hasanzade, M., Inalhan, G., & Tsourdos, A. (2024). Towards global explainability of artificial intelligence agent tactics in close air combat. Aerospace, 11(6), 415.

Sameen, M., Han, K., & Hwang, S. O. (2020). PhishHaven—An efficient real-time AI phishing URLs detection system. Ieee Access, 8, 83425-83443.

Schneier, B. (2018). Artificial intelligence and the attack/defense balance. IEEE security & privacy, 16(02), 96-96.

Schraagen, J. M. (2023). Responsible use of AI in military systems: Prospects and challenges. Ergonomics, 66(11), 1719-1729.

Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83.

Song, H., Park, W., Kim, G., Choi, M. G., In, J. H., Rhee, H., & Kim, K. M. (2024). Memristive explainable artificial intelligence hardware. Advanced Materials, 36(25), 2400977.

Talib, M. A., Majzoub, S., Nasir, Q., & Jamal, D. (2021). A systematic literature review on hardware implementation of artificial intelligence algorithms. The Journal of Supercomputing, 77(2), 1897-1938.

Tien, J. M. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4, 149-178.

Truong, T. C., Diep, Q. B., & Zelinka, I. (2020). Artificial Intelligence in the Cyber Domain: Offense and Defense. Symmetry, 12(3), 410. https://doi.org/10.3390/sym12030410

Wang, Z., Veličković, P., Hennes, D., Tomašev, N., Prince, L., Kaisers, M., ... & Tuyls, K. (2024). TacticAI: an AI assistant for football tactics. Nature communications, 15(1), 1906.

Wei, Y., Zhou, J., Wang, Y., Liu, Y., Liu, Q., Luo, J., ... & Huang, L. (2020). A review of algorithm & hardware design for AI-based biomedical applications. IEEE transactions on biomedical circuits and systems, 14(2), 145-163.

Yu, Z., Abdulghani, A. M., Zahid, A., Heidari, H., Imran, M. A., & Abbasi, Q. H. (2020). An overview of neuromorphic computing for artificial intelligence enabled hardware-based hopfield neural network. Ieee Access, 8, 67085-67099.

Zhang, T., Kong, F., Deng, D., Tang, X., Wu, X., Xu, C., ... & Deng, R. H. (2025). Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey. IEEE Internet of Things Journal.

Zhou, Z., Kuang, X., Sun, L., Zhong, L., & Xu, C. (2020). Endogenous security defense against deductive attack: When artificial intelligence meets active defense for online service. IEEE Communications Magazine, 58(6), 58-64.

Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist—it’s time to make it fair.