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Rita Komalasari
Perpustakaan Institut Pertanian Bogor Jalan Meranti, IPB Kampus Dramaga, Bogor 16681 Telp. (0251) 8621073, Faks. (0251) 8623166
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everaging Advanced Machine Learning for Military Defense: Enhancing Threat Assessment with The LLaMA Model Mustafa, Cecep; Komalasari, Rita
Jurnal Pertahanan: Media Informasi tentang Kajian dan Strategi Pertahanan yang Mengedepankan Identity, Nasionalism dan Integrity Vol 10, No 3 (2024)
Publisher : The Republic of Indonesia Defense University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33172/jp.v10i3.19661

Abstract

In today's dynamic military defense landscape, traditional methods of threat assessment face significant limitations due to the sheer volume and complexity of data generated. The evolving nature of threats demands more efficient, accurate systems to process and detect potential dangers. This challenge has spurred interest in advanced machine learning techniques, particularly large language models (LLMs), to improve detection accuracy and data-handling efficiency. This study explores the integration of the LLaMA model, a state-of-the-art large language model, into existing military threat assessment systems. The main objective is to empirically assess the model's ability to enhance threat detection capabilities and optimize data processing, comparing its effectiveness against traditional approaches. A comprehensive literature review was conducted, analyzing recent empirical and theoretical research on machine learning applications in threat assessment. The comparative analysis measures its efficiency and accuracy relative to conventional methodologies, revealing that integrating the LLaMA model into military defense frameworks significantly improves data processing speed, reduces human error, and enables more accurate identification of emerging threats. Its scalability and adaptability make it a robust solution to limitations in current threat assessment methods. However, implementing the LLaMA model also presents challenges, such as ensuring smooth integration with existing technology infrastructure. The model's reliance on high-quality, domain-specific data necessitates ongoing investments in data curation and maintenance. Additionally, while the automation of routine analysis tasks reduces human error, it prompts questions about the long-term role of human decision-makers, particularly in critical scenarios where human intuition and ethical considerations are paramount. In conclusion, the LLaMA model offers a transformative solution for enhancing threat assessment in military defense. Its ability to process vast data sets quickly, reduce human error, and provide timely insights makes it invaluable. 
Co-Authors Adilah Rifat Hakimah Adriza, Adriza Ahmad Nawawi Andina Dwijayanti Bagaskara, Muhammad Budi Harto Budiman, Diaz Viera Bukhari, Bilal Fiqri Cahyadi Dandun Widhiantoro Deyang Maysa Dhea Putri Ardita Dimas Pramudya Ramadhani Evry, Nur Isnaeni Falahah . Faturizky, Bintang Ramadhana Hadi Putri, Camila Irianti Helmi Fahmi Fauzi Hidayat, Vasha Annisa Hilmi, Arif Intan Farida Yasmin iswanto Laila Felicia Lucy Maulidya Sari, Siti Maulidya, Siti Melinda, Linda Daniati Muhammad Arsyad Mustafa, Cecep Novianti Indah Putri Nurazizah, Fahira Pramesti, Puji Rani Rantika Raysha Damatri Rasyad Roro Santi Saifullah, Razid Alfa Saputri, Rya Ervita Rachma Siti Helmyati Siti Maulidya Sari Sriharyati, Santy Sulaswati, Dinni Sumarni, Teti Suryani, Ai Tiya Yulia Fauziyah Yusnita Zen Munawar