Penelitian ini bertujuan untuk menganalisis kemampuan penetrasi dua jenis proyektil, yaitu WC-6Co dan baja standar Pindad, terhadap pelat baja Armox 500T, yang dikenal memiliki ketahanan tinggi dan digunakan dalam aplikasi pertahanan. Simulasi dilakukan menggunakan perangkat lunak ANSYS Student dengan pendekatan eksplisit dinamik untuk mengevaluasi total kecepatan dan deformasi selama tumbukan proyektil pada pelat. Proyektil WC-6Co menunjukkan kemampuan penetrasi yang signifikan, menembus pelat Armox 500T dengan kecepatan maksimum mencapai 1063,4 m/s, serta menghasilkan deformasi maksimum 3,1779 meter. Sebaliknya, proyektil baja standar Pindad hanya menghasilkan deformasi lokal tanpa penetrasi, dengan kecepatan maksimum 1347,7 m/s dan deformasi maksimum 4,0253 meter. Hasil ini menunjukkan bahwa proyektil berbasis WC-6Co lebih efektif dalam menembus pelat baja Armox 500T dibandingkan dengan proyektil baja Pindad, memberikan wawasan penting bagi pengembangan sistem pertahanan dan pilihan material pelindung.
 

Power Index is a strategic indicator measuring a nation's military, economic, and geopolitical capabilities, closely tied to national defense and security. This study examines key factors such as currency exchange rates, GDP, education levels, and technology penetration in shaping the Power Index. Economic stability underpins defense development, while advanced education and digital infrastructure drive innovations in military technology and cybersecurity. Developed countries dominate defense capabilities due to robust technological infrastructures, while developing nations show potential through investments in education and technology. Beta distribution regression, with its suitability for bounded variables, was utilized to model the Power Index. The study provides insights into the interplay between economic, educational, and technological factors, supporting strategic policymaking to strengthen national defense in a dynamic global landscape.

This study models the inflation rate in Sumatra using Generalized Linear Models (GLM) with Gamma and Negative Binomial distributions. The data includes inflation rates, Consumer Price Index (CPI), poverty rates, and employment sectors from the 2019 BPS report. The results show that the Gamma model performs better in predicting inflation compared to the Negative Binomial model, with a lower AIC value and smaller residual deviance. The CPI variable significantly influences inflation, while other variables are not significant.