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Jurnal Puruhita
Published by Universitas Negeri Semarang
ISSN : -     EISSN : 26559668     DOI : https://doi.org/10.15294/puruhita
Core Subject : Social,
Social Sciences
Jurnal Puruhita is a double blind peer-reviewed journal published by the Universitas Negeri Semarang and managed by the Institute for Research and Community Service, Universitas Negeri Semarang (LPPM UNNES). This journal is published twice a year every February and August and since its publication in 2019 it has used an open access system as a whole. This journal publishes research articles and critical-analytic studies related to empowerment scope of non-learning education, economics, science, technology, language, sports, philosophy, and implications in the field of educational scientific studies.
Arjuna Subject : Ilmu Sosial - Ilmu Sosial (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 7 No. 2 (2025)" : 5 Documents clear
Integrating Smart City Technologies to Enhance Police Performance and Urban Public Safety Cucuk Kristiono
Jurnal Puruhita Vol. 7 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/puruhita.v7i2.37896

Abstract

The rapid growth of smart cities offers new opportunities for enhancing policing efficiency, urban safety, and public trust. This study investigates the integration of smart city technologies—such as IoT-based surveillance, real-time data platforms, intelligent traffic systems, and predictive analytics—into modern law-enforcement operations. Using a mixed-methods approach, the research analyzes operational data from three metropolitan smart city projects, along with 58 interviews involving police officers, urban planners, technology developers, and community representatives. Quantitative findings demonstrate that smart city integration reduces emergency response times by 24%, improves crime detection rates by 31%, and enhances traffic incident management by 42% through automated sensors and connected infrastructure. Qualitative results reveal that police officers benefit from improved situational awareness, yet express concerns about data overload, privacy implications, and insufficient interoperability among digital systems. The study concludes that smart city technologies significantly strengthen public safety when supported by robust governance, ethical data management, and inter-agency collaboration. This research contributes to urban safety science by offering a comprehensive framework for aligning policing functions with smart city infrastructures to achieve sustainable and citizen-centered outcomes. 
Enhancing Police Training and Competency Development through Modern Instructional Frameworks Budi Susanto
Jurnal Puruhita Vol. 7 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/puruhita.v7i2.37898

Abstract

Police training and competency development are central to ensuring effective, ethical, and accountable law-enforcement performance. As public expectations rise and the complexity of policing challenges increases, modern police agencies must adopt training frameworks that emphasize technical proficiency, communication skills, cultural awareness, and critical decision-making. This study examines the effectiveness of contemporary training models by combining survey data from 920 police officers, structured interviews with training instructors, and field observations at three police academies. Quantitative findings indicate that scenario-based training, de-escalation modules, and digital learning tools significantly improve officers’ operational readiness (p < 0.01). Qualitative results suggest that blended learning, mentorship structures, and continuous competency assessments enhance adaptability and long-term performance. Despite progress, challenges persist, including insufficient training time, uneven access to technological resources, and limited emphasis on emotional intelligence and community engagement. The study concludes that integrated, evidence-based training systems—combining pedagogical innovation, technological enhancement, and continuous evaluation—are essential for strengthening policing standards and improving public trust. This research contributes to policing science by providing a structured framework for competency-based training reform and aligning instructional practices with contemporary operational demands. 
Enhancing Law Enforcement Efficiency Through Integrated Smart Policing Systems Setiadi Setiadi
Jurnal Puruhita Vol. 7 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/puruhita.v7i2.37899

Abstract

Smart policing has emerged as a transformative approach to modern law enforcement, leveraging digital technologies, real-time analytics, and interconnected platforms to improve public safety outcomes. This study examines how integrated smart policing systems—comprising IoT sensors, predictive analytics, digital evidence platforms, and automated surveillance networks—reshape police operations in urban environments. Using a mixed-methods design, the research analyzes operational data from three metropolitan areas using smart policing frameworks, supplemented by interviews with 46 police officers, IT specialists, and community stakeholders. Quantitative findings demonstrate a 28% improvement in emergency response times and a 34% increase in crime detection accuracy following the deployment of smart policing tools. Qualitative insights highlight enhanced situational awareness but also emphasize challenges related to data overload, technical interoperability, privacy concerns, and the need for sustained training. The study concludes that smart policing can significantly strengthen law enforcement efficiency when supported by robust governance, ethical data management, and collaborative partnerships. This research contributes to the field of policing studies by providing a comprehensive understanding of how intelligent technologies can be responsibly integrated into daily police operations to build safer communities. 
Enhancing Crime Prevention and Patrol Efficiency Through Geospatial Policing Systems Ririh Dewi Widowati
Jurnal Puruhita Vol. 7 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/puruhita.v7i2.37900

Abstract

Geospatial policing has become a central innovation in modern law enforcement, utilizing geographic information systems (GIS), spatial analytics, and predictive mapping to identify crime patterns and guide strategic resource deployment. This study examines the operational impact, methodological foundations, and challenges of integrating geospatial intelligence into urban policing. A mixed-methods approach was applied, combining analysis of five years of crime data from three metropolitan jurisdictions with interviews involving 44 police officers, GIS analysts, and community representatives. Quantitative results show that geospatial mapping improves hotspot detection accuracy by 29% and reduces response time in high-crime areas by 17% when integrated with targeted patrol strategies. Spatial clustering techniques also enhanced officers’ situational awareness and improved crime prevention planning. Qualitative findings highlight challenges related to data quality, analytical skill gaps, and technological interoperability between GIS platforms and legacy police systems. The study concludes that geospatial policing significantly strengthens proactive law enforcement by generating spatially informed insights, optimizing patrol routes, and supporting evidence-based decision-making. Contributions to policing science include a comprehensive analytic framework for applying geospatial tools in operational contexts and recommendations for improving governance, training, and community transparency. 
Advancing Law Enforcement Efficiency Through Predictive Policing Technologies Rusmiyati Rusmiyati
Jurnal Puruhita Vol. 7 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/puruhita.v7i2.37902

Abstract

Predictive policing has emerged as a transformative approach in modern law enforcement, utilizing statistical modeling, machine learning algorithms, and geospatial analytics to anticipate crime patterns and optimize resource deployment. This study investigates the effectiveness, challenges, and ethical implications of predictive policing systems implemented in urban environments. Using a mixed-methods design, the research analyzes crime data from three metropolitan jurisdictions, supported by interviews with 52 police officers, data analysts, and community stakeholders. Quantitative findings indicate that predictive models enhance hotspot identification accuracy by 33% and reduce targeted-area crime by 18% when combined with proactive patrol strategies. Predictive systems also improve resource allocation by minimizing redundant patrol routes and supporting evidence-based operational planning. However, interviews reveal concerns regarding algorithmic bias, data quality limitations, system opacity, and potential threats to civil liberties. The study concludes that predictive policing can significantly improve law enforcement performance when supported by transparent governance, robust data infrastructure, ethical safeguards, and continuous model evaluation. This research contributes to policing science by providing a comprehensive examination of predictive policing as a practical, technological, and ethical framework for modern public safety management. 

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