Banyaknya lereng pada pembangunan IPAL 1 KIPP IKN menyebabkan adanya potensi longsoran yang membahayakan lingkungan disekitarnya. Faktor yang sering mengakibatkan kelongsoran yaitu pengaruh geometri dan penyusun material tanah lereng tersebut. Tujuan dari penelitian ini adalah menganalisis stabil atau tidaknya suatu lereng yang ditunjukan dalam bentuk nilai Faktor Keamanan. Penelitian menggunakan dua metode, yaitu metode Bishop Simplified dan metode elemen hingga. Data yang digunakan pada penelitian ini yaitu geometri lereng pada jalan akses lingkar kanan IPAL 1 KIPP IKN dari Sta 0+340 s/d Sta 0+380 dan data penyelidikan tanah dari PT. YODYA selaku konsultan perencananya dengan tinggi lereng 25 m, lebar 20,6 m, nilai kohesi tanah (c) = , berat volume tanah , dan sudut geser tanah . Penelitian ini didapatkan hasil Faktor Keamanan dengan metode Bishop Simplified sebesar 1,7 dan metode elemen hingga sebesar 1,7. Berdasarkan nilai Faktor Keamanan yang disarankan SNI-8460 yaitu 1,5 berarti lereng tersebut aman karena nilai Faktor Keamanan lerengnya lebih besar dari yang disarankan.

Radar systems play a crucial role in target tracking within urban environments, where challenges such as clutter, multipath effects, and electromagnetic interference significantly impact detection accuracy. Traditional radar methods often struggle to adapt to dynamic urban conditions, leading to decreased reliability in real-time target tracking. This study aims to develop and evaluate an AI-driven adaptive radar system that enhances tracking accuracy in urban settings. The research employs a quantitative approach using simulations to model radar signal processing under various environmental conditions. The AI model, based on Convolutional Neural Networks (CNN), is trained to optimize radar performance by filtering out noise and dynamically adjusting detection parameters. The results indicate that the AI-based radar system achieves a tracking accuracy of 95.2%, significantly outperforming traditional radar systems, which only reach 80% accuracy. Additionally, the AI-enhanced radar reduces response time to 120 milliseconds, compared to 250 milliseconds in conventional systems, demonstrating improved real-time processing capabilities. The system also exhibits greater resilience to high-clutter environments, maintaining stable target detection despite signal interference. These findings highlight the potential of AI in enhancing radar functionality for applications such as surveillance, traffic monitoring, and security. Future research should focus on integrating AI-driven radar with real-world radar hardware, exploring multi-sensor fusion, and refining adaptive learning techniques to further optimize tracking performance in complex environments