https://jcl.mbicore.com/index.php/JCL en-US arif@amikompurwokerto.ac.id (Arif Mu'amar Wahid, M.Kom.) arif@amikompurwokerto.ac.id (Arif Mu'amar Wahid, M.Kom.) Sat, 08 Mar 2025 00:00:00 +0700 OJS 3.3.0.7 http://blogs.law.harvard.edu/tech/rss 60 https://jcl.mbicore.com/index.php/JCL/article/view/1 This research employs cluster analysis to elucidate patterns in campus killings across the United States, utilizing a comprehensive dataset spanning over two decades. The study systematically categorizes these incidents into distinct clusters based on geographic, temporal, and demographic criteria to identify underlying patterns and potential risk factors associated with campus violence. Through detailed statistical analysis and visualization techniques, the research reveals significant regional disparities and temporal trends in campus violence, highlighting the concentration of incidents in specific areas and periods. Key findings indicate that campus killings are not uniformly distributed geographically or temporally. Instead, they tend to cluster in certain regions—particularly in the northeastern and central United States—with varying incident frequencies over time. The analysis also uncovers notable demographic patterns, demonstrating that certain racial and socio-economic groups are disproportionately affected. These insights are critical for understanding the dynamics of campus violence and can significantly inform policy-making and preventive measures. The study discusses the implications of these findings for legal frameworks and educational policies, suggesting that more targeted, region-specific interventions could enhance campus safety. By integrating cluster analysis with current legislative and policy contexts, the research provides a foundation for data-driven strategies to mitigate campus violence effectively. However, the study acknowledges limitations related to the data's scope and accuracy, which could impact the generalizability of the findings. Future research directions include expanding the analysis to international contexts, integrating qualitative data, conducting longitudinal studies to assess policy effectiveness, and exploring technological advancements for predictive analytics in campus safety. This research contributes to the academic discourse on campus safety by offering a methodologically robust analysis that links empirical data with policy implications, highlighting the potential for informed legislative actions to foster safer educational environments. Agung Budi Prasetio, Burhanuddin bin Mohd Aboobaider, Asmala bin Ahmad Copyright (c) 2025 Journal of Cyber Law https://jcl.mbicore.com/index.php/JCL/article/view/1 Sat, 15 Mar 2025 00:00:00 +0700 https://jcl.mbicore.com/index.php/JCL/article/view/2 This study utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to analyze and map the geographical distribution of gun violence across the United States, drawing on data sourced from the Gun Violence Archive. By identifying distinct clusters of gun violence incidents, the research highlights significant spatial patterns and hotspots, particularly in major urban centers such as Los Angeles, Phoenix, Chicago, and New York. These findings underscore the correlation between gun violence and urban density, socio-economic factors, and the distribution of firearm accessibility. The study also discusses the implications of these spatial patterns for public safety and legal frameworks, advocating for targeted policy interventions and resource allocation to areas most affected by gun violence. Additionally, the research addresses the limitations of the current dataset and the DBSCAN method, proposing future research directions that incorporate a broader range of data sources and advanced analytical techniques. This paper aims to provide policymakers and law enforcement agencies with actionable insights to develop more effective gun control measures and violence prevention strategies. Latasha Lenus, Andhika Rafi Hananto Copyright (c) 2025 Journal of Cyber Law https://jcl.mbicore.com/index.php/JCL/article/view/2 Sat, 15 Mar 2025 00:00:00 +0700 https://jcl.mbicore.com/index.php/JCL/article/view/3 This study investigates crime trends and regional clusters in Russia from 2008 to 2023, utilizing data mining techniques to uncover patterns and inform policy-making, particularly in the context of cyber law. Time series analysis reveals a consistent decline in overall crime rates, with theft dominating but steadily decreasing, while violent crimes such as murder show gradual declines. Through K-Means clustering, the regions are categorized into four distinct clusters, each reflecting unique socio-economic and geographic dynamics. Cluster 0 encompasses rural, low-crime regions, characterized by geographic isolation and sparse populations. Cluster 1, including urbanized and industrial regions, shows high rates of property and violent crimes. Cluster 2, represented solely by Moscow, exhibits extreme crime intensity, underlining the complexities of managing crime in a metropolitan hub. Cluster 3 features transitional regions with moderate crime levels, highlighting a mix of rural and urban influences. The findings underscore the interconnectedness of traditional crime patterns and vulnerabilities to cybercrime. Urbanized clusters face heightened exposure to digital threats, while rural regions are vulnerable to targeted scams due to limited digital infrastructure. These insights advocate for tailored legal frameworks, balancing urban-focused cybersecurity policies with rural community-based interventions. However, the study acknowledges the dataset's limitation in excluding direct cybercrime indicators, necessitating further integration with digital offense data for comprehensive insights. This research contributes to bridging the gap between traditional criminology and cyber law by emphasizing the importance of data-driven governance. By identifying regional disparities and crime dynamics, it highlights the need for adaptive legal frameworks that respond to evolving socio-economic and technological landscapes. Future work should integrate cybercrime datasets and refine clustering techniques to enhance granularity and address cross-border crime dynamics. Jeffri Prayitno Bangkit Saputra, Aayush Kumar Copyright (c) 2025 Journal of Cyber Law https://jcl.mbicore.com/index.php/JCL/article/view/3 Sat, 15 Mar 2025 00:00:00 +0700 https://jcl.mbicore.com/index.php/JCL/article/view/4 This study explores the temporal and spatial patterns of crime through the application of seasonal decomposition and clustering techniques, providing actionable insights for law enforcement and policymakers. Using a dataset of reported crimes, the research dissects the data into trend, seasonal, and residual components using the Seasonal and Trend decomposition using Loess (STL) methodology. The analysis reveals long-term crime trends, recurring seasonal fluctuations, and anomalies that warrant targeted interventions. Furthermore, k-means clustering is employed to identify high-crime and low-crime periods, offering a granular understanding of crime dynamics across time. Geospatial visualization complements the analysis, illustrating crime clusters within urban hotspots and highlighting areas of concentrated criminal activity. The findings underscore the critical role of temporal and spatial analytics in crime prevention, demonstrating how patterns in reported crimes can guide resource allocation and strategic planning. Moreover, the study bridges the gap between traditional crime analysis and the emerging field of cybercrime prevention. By extending these methodologies to the digital domain, the research highlights their potential application in analyzing cyber threats, such as ransomware campaigns or phishing attacks, which often exhibit temporal regularities and geographic dispersion. This interdisciplinary approach advocates for the integration of data-driven methods into law enforcement strategies and legislative frameworks, emphasizing the importance of adaptive policies in addressing both physical and digital crime landscapes. Future work aims to incorporate advanced predictive models and external data sources to deepen insights into crime causation and prevention. The research contributes to the evolving discourse on smart policing and adaptive cybercrime legislation, paving the way for safer and more resilient societies in both urban and virtual environments. Agung Darmawan Buchdadi, Ammar Salamh Mujali Al-Rawahna Copyright (c) 2025 Journal of Cyber Law https://jcl.mbicore.com/index.php/JCL/article/view/4 Sat, 15 Mar 2025 00:00:00 +0700 https://jcl.mbicore.com/index.php/JCL/article/view/5 The rapid increase in online transactions has significantly raised the risk of fraudulent activities, leading to substantial financial losses. Traditional fraud detection methods often struggle to address the complexity and scale of modern digital fraud. This paper explores the application of machine learning techniques, specifically Random Forest and Gradient Boosting, to detect fraudulent transactions. Both algorithms are widely recognized for their ability to handle large, complex datasets and improve predictive accuracy. The study examines how these techniques work, with Random Forest focusing on ensemble learning and feature importance, and Gradient Boosting employing an iterative, stage-wise approach to correct errors from previous models. Key challenges in fraud detection, including class imbalance, data scarcity, the evolving nature of fraud, and high-dimensional data, are discussed in depth. The paper reviews relevant studies that have utilized machine learning for fraud detection in various contexts, including e-commerce and credit card fraud, highlighting the strengths and limitations of different approaches. It also examines strategies to mitigate challenges, such as resampling techniques and continuous learning. The findings emphasize that while machine learning offers significant improvements in fraud detection, continuous adaptation is essential to keep pace with evolving fraud tactics. By providing a comprehensive overview of machine learning in fraud detection, this research contributes valuable insights into enhancing security measures for digital transactions and financial systems. Satrya Fajri Pratama, Arif Mu'amar Wahid Copyright (c) 2025 Journal of Cyber Law https://jcl.mbicore.com/index.php/JCL/article/view/5 Sat, 15 Mar 2025 00:00:00 +0700