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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 11 Documents
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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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