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All Journal Perkara: Jurnal Ilmu Hukum Dan Politik Jaksa: Jurnal Kajian Ilmu Hukum Dan Politik
Rengga Kusuma Putra
Universitas Sains dan Teknologi Komputer, Semarang, Jawa Tengah, Indonesia
Humanities Law, Crime, Criminology & Criminal Justice Social Sciences

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Kategorisasi Hukum Deepfake Politik untuk Menentukan Batas Pertanggungjawaban atas Manipulasi Konten Digital Sri Yulianingsih; Rengga Kusuma Putra; Ahmad Zainudin
Perkara : Jurnal Ilmu Hukum dan Politik Vol. 4 No. 3 (2026): September: Jurnal Ilmu Hukum dan Politik (PERKARA)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/89yn4y56

Abstract

The development of generative artificial intelligence has accelerated the use of deepfakes in political communication while raising complex questions concerning the boundaries between freedom of expression, information manipulation, and legal accountability. These challenges arise because the synthetic nature of digital content does not, by itself, establish legal unlawfulness. This study aims to develop a legal categorisation framework for political deepfakes to determine the boundaries of legal accountability based on the characteristics and context of their use. The study employs normative legal research with qualitative and comparative legal approaches. Legal materials are analysed through six dimensions, namely nature, context, intent, deception, dissemination, and harm, which are subsequently mapped into normative categories according to their relevance to applicable legal norms. The findings demonstrate that political deepfakes should not be assessed through a binary distinction between legal and illegal content. The analysis establishes four normative categories: legitimate political expression, political satire, informational manipulation, and legally accountable deepfake. These categories form a continuum of legal responsibility that enables a more proportionate assessment of the legal relevance of synthetic political content. The stronger the elements of intent, deception, dissemination, and harm, the greater the potential for legal accountability. This framework provides a conceptual basis for developing regulation and law enforcement approaches that safeguard information integrity and democratic processes without imposing disproportionate restrictions on political freedom of expression.
Pola Konsistensi Tuntutan Jaksa dalam Perkara Korporasi: Analisis Data Putusan Berbasis Machine Learning Berliant Pratiwi; Rengga Kusuma Putra
Jaksa : Jurnal Kajian Ilmu Hukum dan Politik Vol. 4 No. 3 (2026): JULI: Jurnal Kajian Ilmu Hukum dan Politik (JAKSA)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/v8666444

Abstract

The increasing complexity of corporate crime has heightened the need for consistent prosecutorial practices to ensure legal certainty, fairness, and accountability within the criminal justice system. However, empirical studies examining the consistency of prosecutorial demands in corporate criminal cases remain limited, particularly those employing data-driven analytical approaches. This study aims to analyze the consistency patterns of prosecutors' demands in corporate criminal cases using court decision data and a Machine Learning approach. The research employed a quantitative design based on data-driven legal analytics, utilizing corporate criminal court decisions as the unit of analysis. A Random Forest algorithm was developed to identify demand patterns based on case characteristics, including the type of offense, financial loss, corporate benefit, management involvement, recovery efforts, and aggravating or mitigating circumstances. The findings indicate that the proposed model achieved high predictive performance, demonstrating that prosecutorial demands generally follow consistent patterns influenced primarily by the magnitude of financial loss, the type of offense, and the economic benefits obtained by the corporation. These findings highlight the potential of Machine Learning to provide an objective framework for evaluating prosecutorial consistency and supporting evidence-based prosecutorial policies. This study contributes to the advancement of computational legal studies by introducing a data-driven framework for assessing prosecutorial discretion in corporate criminal cases and offers practical implications for improving transparency, accountability, and consistency in prosecution practices.
Co-Authors Ahmad Zainudin Berliant Pratiwi Sri Yulianingsih