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Predicting Interprovincial Rice Food Security in Indonesia as a Pillar of National Defense Using the Random Forest Regressor Algorithm Bagus Hendra Saputra; Ahmad Eryan Firdaus
Journal of Defense Technology and Engineering Vol. 1 No. 1 (2025): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

This study investigates interprovincial rice food security in Indonesia as a strategic pillar of national defense. Using a quantitative predictive approach, the Random Forest Regressor algorithm was applied to multidimensional data from all provinces, incorporating variables such as rice expenditure per capita, rice price, production, population, consumption, and harvested area. The results show significant disparities between provinces: surplus regions such as East Java, Lampung, and South Sulawesi contrast sharply with deficit areas like Jakarta, Papua, and Bangka Belitung. Feature importance analysis reveals that supply-side factors, particularly harvested area (50.5%) and production (33.2%), are the most decisive, while demand-side factors have weaker influence. Model evaluation achieved an R² of 0.8239, confirming strong predictive reliability. These findings underscore that rice food security is not only an economic and social issue but also a critical aspect of non-military defense. Strengthening predictive systems and interprovincial distribution networks is essential to ensure resilience against disruptions from disasters, conflicts, or geopolitical instability. The study highlights the practical value of machine learning models in guiding evidence-based policy to secure national food sovereignty.
Cryptographic algorithm optimization for defense data security using quantum inspired algorithms Bagus Hendra Saputra; Jonson Manurung; Jeremia Paskah Sinaga
Journal of Defense Technology and Engineering Vol. 1 No. 2 (2026): January, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

The rapid advancement of quantum computing poses a critical threat to classical public-key cryptographic systems widely used in defense communication infrastructures, while the practical deployment of post-quantum cryptography (PQC) remains constrained by excessive key sizes, computational overhead, and energy consumption in bandwidth- and latency-sensitive military environments. This study aims to develop and evaluate a quantum-inspired multi-objective optimization framework to enhance the operational feasibility of standardized PQC schemes without compromising cryptographic security. The proposed method applies a Quantum Genetic Algorithm (QGA) to optimize configuration parameters of CRYSTALS-Kyber and CRYSTALS-Dilithium by simultaneously balancing security strength, computational performance, resource efficiency, and deployability. Experiments were conducted using official NIST test vectors and defense-oriented communication scenarios, with performance evaluated across encryption and signature latency, throughput, key and signature sizes, memory footprint, and energy consumption, while security was validated against classical and quantum attack models. The results demonstrate that the optimized configurations achieve key and signature size reductions of up to 10.3%, throughput improvements of up to 15.5%, and energy consumption reductions of up to 12.5% compared to baseline NIST implementations, while fully maintaining NIST security levels and robust resistance to quantum adversaries. These improvements significantly enhance the suitability of PQC for tactical radios, satellite communications, and resource-constrained defense platforms. The findings indicate that quantum-inspired multi-objective optimization is a critical enabler for transitioning post-quantum cryptography from theoretical security constructs to deployable, mission-ready solutions in real-world defense systems.