The increasing use of mobile devices has increased the risk of data theft, posing significant security challenges for individuals and organizations. This study proposes an early detection system for data theft on mobile devices using machine learning algorithms. The system is designed to identify suspicious patterns in application usage, network access, and CPU/memory activity, providing early warnings to prevent potential data loss. By employing algorithms such as Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the developed models demonstrated significant performance: CNN achieved the highest accuracy of 95.1%, with a precision of 94.2%, recall of 93.5% , F1-score of 93.8%, and AUC-ROC of 0.96. Random Forest and SVM also showed competitive performance with accuracy rates of 94.7% and 92.5%, respectively. These findings highlight the high potential of machine learning algorithms for real-time detection of data theft threats, providing adaptive protection against evolving cyberattack methods. This approach offers a promising solution to strengthen mobile device security frameworks and safeguard user data against increasingly sophisticated cyber threats.

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

This study examines the compliance of law enforcement officers with human rights (HR) principles during investigative practices in Indonesia using a quantitative approach. Despite Indonesia's commitment to HR protection, significant gaps persist in the enforcement of rights, such as access to legal counsel and humane treatment. The study aims to evaluate the level of HR compliance, identify influencing factors, and propose actionable solutions to improve investigative practices. The research employed a survey methodology involving 400 respondents, including law enforcement officers, suspects, witnesses, and lawyers. Key indicators analyzed include the treatment of detainees, the right to information, access to legal representation, and mechanisms for reporting HR violations. The collected data were analyzed statistically using descriptive and inferential methods to identify compliance patterns and underlying factors. Findings reveal that HR compliance during investigations remains moderate. While 62% of respondents acknowledged adherence to the right to information, only 45% reported consistent access to legal representation, and 38% experienced inhumane treatment. Factors such as officer training, independent oversight mechanisms, and the integration of technology positively influence compliance. However, challenges like case resolution pressure and limited public awareness of reporting mechanisms hinder broader compliance. The study contributes theoretically by highlighting the multifaceted nature of HR compliance, emphasizing the roles of training, oversight, and technology. Practically, it provides recommendations for policymakers to enhance HR-focused training, promote the use of technology in investigations, and improve the accessibility and transparency of reporting systems. Limitations include geographical constraints and potential biases in self-reported data, suggesting avenues for future research to adopt longitudinal approaches and broader samples. This research advances understanding of HR compliance in law enforcement and offers practical insights for fostering fair and HR-centered investigative practices in Indonesia.