Technological and scientific collaboration in mitigating CBRNE threats in Indonesia

Authors

  • Faonaso Harefa Defense University, Indonesia
  • Cecilia F. Harsono Defense University, Indonesia

DOI:

https://doi.org/10.35335/lebah.v19i3.491

Keywords:

Collaboration, Defense Health, Mitigation, Technology, National Security, and Science

Abstract

Latar Belakang dan Celah Riset: Ancaman Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) bersifat tidak kasatmata, cepat menyebar, dan berdampak sistemik, sementara pemanfaatan teknologi deteksi modern dan integrasi data lintas sektor dalam sistem mitigasi CBRNE masih belum terkonseptualisasi secara utuh dalam kerangka sains pertahanan. Literatur yang ada cenderung membahas teknologi secara parsial tanpa mengaitkannya dengan tata kelola koordinasi dan respons nasional. Tujuan dan Metodologi: Penelitian ini bertujuan menganalisis peran perangkat deteksi modern dan integrasi data meliputi data sharing, Early Warning System (EWS), dan geospasial pertahanandalam meningkatkan koordinasi interinstansi dan kecepatan respons CBRNE. Penelitian menggunakan pendekatan kualitatif deskriptif melalui analisis literatur, dokumen internasional, teori pertahanan, serta temuan empiris mutakhir terkait integrasi sensor cerdas dan kecerdasan buatan dalam deteksi CBRNE. Temuan Konseptual dan Kontribusi Teoretis: Hasil analisis menunjukkan bahwa biosensor, detektor radiasi, drone berbasis AI, dan sistem geospasial secara konseptual meningkatkan akurasi identifikasi, jangkauan pemantauan, dan kecepatan pengambilan keputusan ketika diintegrasikan dalam sistem EWS dan data sharing lintas sektor. Penelitian ini berkontribusi secara teoretis dengan menegaskan bahwa efektivitas mitigasi CBRNE tidak hanya ditentukan oleh kecanggihan teknologi, tetapi oleh integrasi teknologi tersebut ke dalam tata kelola sains pertahanan yang memungkinkan interoperabilitas, koordinasi, dan respons nasional yang cepat dan adaptif

References

Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. Npj Digital Medicine, 4(1). https://doi.org/10.1038/s41746-021-00438-z

Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. Q. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, 1–35. https://doi.org/10.1093/database/baaa010

Almalawi, A., Khan, A. I., Alsolami, F., Abushark, Y. B., & Alfakeeh, A. S. (2023). Managing Security of Healthcare Data for a Modern Healthcare System. Sensors, 23(7), 1–18. https://doi.org/10.3390/s23073612

Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 1–9. https://doi.org/10.1186/s12911-020-01332-6

Amiri, Z., Heidari, A., Navimipour, N. J., Esmaeilpour, M., & Yazdani, Y. (2024). The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review. In Neural Computing and Applications (Vol. 36, Issue 11). Springer London. https://doi.org/10.1007/s00521-023-09366-3

Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G. (2020). A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning. Archives of Computational Methods in Engineering, 27(4), 1071–1092. https://doi.org/10.1007/s11831-019-09344-w

Doyle, L., McCabe, C., Keogh, B., Brady, A., & McCann, M. (2020). An overview of the qualitative descriptive design within nursing research. Journal of Research in Nursing, 25(5), 443–455. https://doi.org/10.1177/1744987119880234

El Arab, R. A., Abu-Mahfouz, M. S., Abuadas, F. H., Alzghoul, H., Almari, M., Ghannam, A., & Seweid, M. M. (2025). Bridging the Gap: From AI Success in Clinical Trials to Real-World Healthcare Implementation—A Narrative Review. Healthcare (Switzerland), 13(7), 1–13. https://doi.org/10.3390/healthcare13070701

El Arab, R. A., Almoosa, Z., Alkhunaizi, M., Abuadas, F. H., & Somerville, J. (2025). Artificial intelligence in hospital infection prevention: an integrative review. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1547450

Faiyazuddin, M., Rahman, S. J. Q., Anand, G., Siddiqui, R. K., Mehta, R., Khatib, M. N., Gaidhane, S., Zahiruddin, Q. S., Hussain, A., & Sah, R. (2025). The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency. Health Science Reports, 8(1), 1–18. https://doi.org/10.1002/hsr2.70312

Goktas, P., & Grzybowski, A. (2025). Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI. Journal of Clinical Medicine, 14(5), 1–28. https://doi.org/10.3390/jcm14051605

Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting and Social Change, 162(June 2020), 120392. https://doi.org/10.1016/j.techfore.2020.120392

Lameesa, A., Hoque, M., Alam, M. S. Bin, Ahmed, S. F., & Gandomi, A. H. (2024). Role of metaheuristic algorithms in healthcare: a comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health. Journal of Computational Design and Engineering, 11(3), 223–247. https://doi.org/10.1093/jcde/qwae046

Liopyris, K., Gregoriou, S., Dias, J., & Stratigos, A. J. (2022). Artificial Intelligence in Dermatology: Challenges and Perspectives. Dermatology and Therapy, 12(12), 2637–2651. https://doi.org/10.1007/s13555-022-00833-8

Ma, R., & Li, M. (2025). Assessment of Land Resource Utilization Efficiency, Spatiotemporal Pattern, and Network Characteristics in Resource-Based Regions: A Case Study of Shanxi Province. Sustainability (Switzerland), 17(6). https://doi.org/10.3390/su17062458

Maleki Varnosfaderani, S., & Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11(4), 1–38. https://doi.org/10.3390/bioengineering11040337

Marot, A., Kelly, A., Naglic, M., Barbesant, V., Cremer, J., Stefanov, A., & Viebahn, J. (2022). Perspectives on Future Power System Control Centers for Energy Transition. Journal of Modern Power Systems and Clean Energy, 10(2), 328–344. https://doi.org/10.35833/MPCE.2021.000673

Martini, B., Bellisario, D., & Coletti, P. (2024). Human-Centered and Sustainable Artificial Intelligence in Industry 5.0: Challenges and Perspectives. Sustainability (Switzerland) , 16(13). https://doi.org/10.3390/su16135448

Mhlanga, D. (2022). Human-Centered Artificial Intelligence: The Superlative Approach to Achieve Sustainable Development Goals in the Fourth Industrial Revolution. Sustainability (Switzerland), 14(13). https://doi.org/10.3390/su14137804

Musarat, M. A., Irfan, M., Alaloul, W. S., Maqsoom, A., & Ghufran, M. (2023). A Review on the Way Forward in Construction through Industrial Revolution 5.0. Sustainability (Switzerland), 15(18). https://doi.org/10.3390/su151813862

Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020). Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies in medical imaging. The BMJ, 368, 1–12. https://doi.org/10.1136/bmj.m689

Nahavandi, S. (2019). Industry 5 . 0. Sustainability, 11, 43–71.

Najdenko, E., Riedel, V., Dittert, K., Ruckelshausen, A., Lorenz, F., & Olfs, H. W. (2025). A Rapid In-Field Soil Extraction Procedure to Measure Plant-Available Soil P and K Using an ISFET Multi-Sensor. Journal of Plant Nutrition and Soil Science, 593–603. https://doi.org/10.1002/jpln.12007

Park, J. H. (2020). Effects of nurses’ patient safety management importance, patient safety culture and nursing service quality on patient safety management activities in tertiary hospitals. Journal of Korean Academy of Nursing Administration, 26(3), 181–191. https://doi.org/10.11111/JKANA.2020.26.3.181

Paschen, U., Pitt, C., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147–155. https://doi.org/10.1016/j.bushor.2019.10.004

Rani, S., Kumar, R., Panda, B. S., Kumar, R., Muften, N. F., Abass, M. A., & Lozanović, J. (2025). Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications. Diagnostics, 15(15), 1914. https://doi.org/10.3390/diagnostics15151914

Rudko, I., Bonab, A. B., & Bellini, F. (2021). Organizational structure and artificial intelligence. Modeling the intraorganizational response to the AI contingency. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2341–2364. https://doi.org/10.3390/jtaer16060129

Salam, A., & Abhinesh, N. (2024). Revolutionizing dermatology: The role of artificial intelligence in clinical practice. IP Indian Journal of Clinical and Experimental Dermatology, 10(2), 107–112. https://doi.org/10.18231/j.ijced.2024.021

Sebele-Mpofu, F. Y. (2020). Saturation controversy in qualitative research: Complexities and underlying assumptions. A literature review. Cogent Social Sciences, 6(1). https://doi.org/10.1080/23311886.2020.1838706

Taj, I., & Jhanjhi, N. Z. (2022). Towards Industrial Revolution 5.0 and Explainable Artificial Intelligence: Challenges and Opportunities. International Journal of Computing and Digital Systems, 12(1), 285–310. https://doi.org/10.12785/ijcds/120124

Tang, L., Li, J., & Fantus, S. (2023). Medical artificial intelligence ethics: A systematic review of empirical studies. Digital Health, 9. https://doi.org/10.1177/20552076231186064

Trocin, C., Mikalef, P., Papamitsiou, Z., & Conboy, K. (2023). Responsible AI for Digital Health: a Synthesis and a Research Agenda. Information Systems Frontiers, 25(6), 2139–2157. https://doi.org/10.1007/s10796-021-10146-4

Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and Design in the Age of Artificial Intelligence. Journal of Product Innovation Management, 37(3), 212–227. https://doi.org/10.1111/jpim.12523

Zhai, K., Yousef, M. S., Mohammed, S., Al-Dewik, N. I., & Qoronfleh, M. W. (2023). Optimizing Clinical Workflow Using Precision Medicine and Advanced Data Analytics. Processes, 11(3). https://doi.org/10.3390/pr11030939

Zhang, J. (2025). The Application of Artificial Intelligence Technology in Human Centered Manufacturing in Industry 5.0. Scalable Computing: Practice and Experience, 26(3), 1242–1256. https://doi.org/10.12694/scpe.v26i3.4130

Zheng, X., Zheng, K., Wen, Y., Meng, J., Zhang, X., Wen, X., Zhao, Z., Zheng, C., Cai, X., Lin, J., Chen, J., Duan, J., Jiang, L., Yuan, W., Li, X., Xie, D., Cai, Y., Zhang, J., & Cai, M. (2025). An end-to-end multifunctional AI platform for intraoperative diagnosis. Npj Digital Medicine, 8(1), 1–12. https://doi.org/10.1038/s41746-025-01808-7

Ziatdinov, R., Atteraya, M. S., & Nabiyev, R. (2024). The Fifth Industrial Revolution as a Transformative Step towards Society 5.0. Societies, 14(2), 1–15. https://doi.org/10.3390/soc14020019

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Published

2026-01-30

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