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Article 20 of the Personal Data Protection Law (UU PDP) normatively regulates the protection of data subjects and constitutes a key provision emphasizing the principle of consent as the legal basis for the processing of personal data for commercial purposes. This norm safeguards individual privacy rights, including the validity of explicit consent, the right to withdraw consent, and the responsibility of data controllers. Furthermore, Articles 65–70 of the UU PDP substantively regulate various forms of criminal violations in the context of personal data protection, including the acquisition, disclosure, processing, falsification, and illegal use of personal data, demonstrating the state’s commitment to providing criminal law protection against personal data breaches. This study employs doctrinal legal research with a descriptive-normative approach to address two main issues. First, it aims to examine and identify the legal protection of personal data trading under the UU PDP. Second, it seeks to analyze and identify the legal certainty of personal data trading as regulated by the UU PDP. Although Article 20 of the UU PDP is intended to provide strong protection against the commercial use of personal data, the provision still gives rise to legal uncertainty due to the lack of clear regulation of several crucial aspects. Similarly, Articles 65–70 contain significant legal uncertainties, particularly with regard to the elements of criminal offenses, forms of harm, corporate criminal liability, overlap with other regulations, and the absence of clear enforcement mechanisms and implementing institutions.  

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

Article 20 of the Personal Data Protection Law (PDP Law) normatively regulates the protection of data subjects and constitutes a pivotal provision that underscores the principle of consent as the legal basis for the processing of personal data for commercial purposes. This normative framework safeguards individuals’ right to privacy, encompassing the validity of explicit consent, the right to withdraw such consent, and the accountability of data controllers. In parallel, Articles 65 to 70 of the PDP Law substantively establish various categories of criminal offenses within the context of personal data protection. These include the unlawful acquisition, disclosure, processing, falsification, and utilization of personal data. Collectively, these provisions reflect the State’s commitment to ensuring criminal legal protection against violations of personal data.This research adopts a doctrinal legal methodology with a descriptive-normative approach to address two primary issues: First, to examine and identify the scope of legal protection concerning the trade of personal data under the PDP Law; and Second, to assess and determine the degree of legal certainty afforded to the trade of personal data within the framework of the PDP Law.Notwithstanding the protective intent embodied in Article 20—particularly concerning the commercial use of personal data—this provision gives rise to legal uncertainty due to the absence of comprehensive regulatory clarity on several critical aspects. Similarly, Articles 65 to 70 continue to exhibit significant legal ambiguities, particularly with respect to the constituent elements of offenses, the definition of harm, corporate liability, regulatory overlap with other legislative instruments, and the lack of clear enforcement mechanisms and implementing institutions.

The rapid advancement of artificial intelligence (AI) in the digital age offers substantial benefits by enhancing efficiency and productivity. Nevertheless, these developments also pose significant challenges to the protection of human rights. Issues such as privacy violations, algorithmic bias, discrimination, and opaque automated decision-making highlight the need for a strong integration of ethical values and legal frameworks in the use of AI. This study applies a normative legal method supported by literature-based research to examine the existing regulatory frameworks and the ethical principles underpinning them. The findings indicate that ethical principles such as transparency, accountability, fairness, and human-centeredness serve as essential moral guidelines to prevent AI misuse. Meanwhile, legal rules ensure certainty, establish accountability mechanisms, and provide sanctions for violations. The synergy between ethics and law forms a crucial foundation to ensure that technological innovation aligns with the protection of human rights, upholds human dignity, and supports the creation of a safe and just digital environment

Big data platforms face significant challenges related to cybersecurity and privacy due to the vast volume, variety, and velocity of data they manage. Traditional static security measures often fail to address the dynamic and complex nature of big data environments. This research proposes an adaptive cybersecurity framework that integrates dynamic access control and differential privacy mechanisms to enhance both the security and privacy of big data platforms. The dynamic access control mechanism continuously adjusts access permissions in real-time based on changing risk and trust levels, ensuring that sensitive data remains secure even as user roles and data flows evolve. The differential privacy mechanism adds noise to data, preserving individual privacy while allowing for meaningful data analysis. Through simulations and case studies, the framework was evaluated in various real-world environments, including healthcare, IoT, and finance, where it demonstrated scalability, efficiency, and robust security performance. The results showed that the proposed framework significantly reduced unauthorized access attempts and maintained data privacy, while still enabling effective data analysis. Although there were some challenges regarding performance overhead, particularly in resource-constrained environments, the framework remained effective in large-scale systems. The findings highlight the importance of adaptive security practices in big data environments and suggest that future research should focus on refining dynamic security mechanisms and applying differential privacy in diverse real-world scenarios. These advancements are essential for ensuring that big data platforms can handle evolving cyber threats without compromising data utility or privacy.

This study investigates the role of Information Technology (IT) governance in enhancing risk management performance and ensuring regulatory compliance within multinational digital enterprises. As digital transformation continues to reshape the global business landscape, organizations face increasing challenges in managing technological risks and complying with complex regulatory requirements across various jurisdictions. The study adopts a quantitative approach, using a survey methodology to collect data from senior IT and compliance managers in multinational digital enterprises. The survey focuses on how IT governance frameworks, such as COBIT 2019 and ISO 27000, are utilized to align IT strategies with business objectives, mitigate risks, and maintain regulatory compliance. The findings indicate that organizations with well-established IT governance structures are better positioned to proactively identify and mitigate risks, ensuring greater consistency in meeting regulatory requirements. These organizations demonstrate improved risk management effectiveness, especially concerning cybersecurity, data privacy, and compliance with global regulations like GDPR. In contrast, organizations with ad hoc or decentralized governance structures struggle with fragmented risk management and compliance efforts. The study further highlights the importance of integrating IT governance frameworks with internal audit functions, specifically the Chief Audit Executive (CAE), to enhance cybersecurity resilience and ensure compliance with global standards. This research contributes to the literature by providing empirical evidence on the integration of IT governance, risk management, and regulatory compliance in multinational enterprises. It also highlights the need for a structured and systematic approach to IT governance to improve organizational performance in managing risks and ensuring consistent regulatory adherence. The study offers practical insights for organizations looking to optimize their IT governance structures in the face of rapid digital transformation.

In the ever-evolving digital era, the importance of cyber law in safeguarding user privacy cannot be overstated. As a distinct legal discipline, cyber law examines regulations that oversee activities in cyberspace, offering legal protection regarding personal data and user privacy. Referring to various credible sources, this article evaluates the role of cyber law in protecting user privacy in the Indonesian context. The article discusses regulations such as the Electronic Information and Transactions Law (UU ITE), the Government Regulation on Electronic Systems, and the Personal Data Protection Bill. The main elements analyzed include supervision, regulation, resolution, and protection of user rights when facing digital security challenges. The examination shows that cyber law plays a significant role in protecting individuals' personal information, thereby encouraging safer and more trustworthy digital services. Furthermore, the article points out the various challenges faced in enforcing cyber law, such as administrative constraints, inadequate digital literacy, and limited access to justice, especially for those without legal knowledge or support. Consequently, to improve the effectiveness of cyber law, continued efforts are needed to strengthen the digital regulatory framework and provide more comprehensive user privacy protection. This perspective seeks to enhance understanding of the significance of cyber law in safeguarding privacy and proposes recommendations for governments and relevant organizations in addressing privacy issues in the digital age.

The development of social media has encouraged students to be more active in interacting, sharing information, and establishing relationships online. However, the high intensity of social media use also increases the risk of personal data leakage. This literature study aims to examine the relationship between privacy awareness and students' safe behavior in social media use. The research was conducted by examining various sources, including scientific journals, articles, research reports, books, and online references relevant to digital security issues. The results of the study show that privacy awareness has a significant role in shaping students' safe behavior. This awareness is reflected through the practice of using strong passwords, the implementation of two-factor authentication, and prudence in sharing personal information in the digital space. Although knowledge about digital threats is quite adequate, students' safe behavior is still influenced by the level of internalization of privacy awareness. Therefore, increasing privacy awareness requires not only information, but also educational strategies that emphasize the formation of consistent digital attitudes and habits. This study makes an important contribution to understanding the relationship between privacy awareness and safe behavior, as well as being the basis for the development of more effective digital literacy programs among students.

This research investigates the development of IT-based Automatic Identification System (AIS) data surveillance models supporting maritime safety through integration of advanced information technology, maritime engineering principles, and human factors optimization. AIS technology generates vast real-time vessel movement data creating unprecedented opportunities for safety enhancement through systematic surveillance, collision risk detection, traffic pattern analysis, and incident prevention, yet effectiveness depends critically on intelligent data processing algorithms, reliable IT infrastructure, and competent personnel capable of interpreting surveillance outputs and taking appropriate actions. Through qualitative analysis involving maritime safety authorities, vessel traffic service (VTS) operators, port authorities, marine engineers, IT specialists, data scientists, and maritime training institutions, this study examines how IT-based surveillance models incorporating pattern recognition, anomaly detection, predictive analytics, and crew-centered interfaces can transform maritime safety management from reactive incident response toward proactive risk prevention. Results demonstrate that intelligent AIS surveillance can identify 75-90% of high-risk situations 15-45 minutes before critical events, reduce collision risks by 60-80%, improve traffic management efficiency by 35-55%, and enhance crew situational awareness by 45-65% when integrated with appropriate training programs developing personnel competencies in data interpretation, system operation, and coordinated response. Key implementation challenges include data quality and completeness issues, computational infrastructure requirements, algorithm development complexity, personnel competency gaps requiring substantial training investments, organizational coordination barriers, and privacy/security concerns. Findings reveal that successful AIS surveillance implementation requires holistic sociotechnical approaches integrating IT systems engineering, maritime domain expertise, and human capability development through coordinated design, deployment, and training strategies. This research contributes to maritime safety literature by providing integrated frameworks for IT-based surveillance systems incorporating technical capabilities, operational requirements, and human factors supporting evidence-based safety management.

The implementation of the Standard Inpatient Class (KRIS) policy presents challenges for hospitals in ensuring equitable service delivery. Organizational readiness depends not only on adequate physical infrastructure but also on an adaptive organizational culture and strong clinical leadership to guide change. As a referral hospital, RS BM must ensure alignment across organizational components to meet KRIS standards sustainably. This study analyzes the influence of infrastructure quality and clinical leadership on organizational readiness for KRIS implementation, with organizational culture as an intervening variable. A quantitative cross-sectional design was used, involving 136 healthcare and medical personnel working in the inpatient units of RS Bhakti Mulia. Data were collected through structured questionnaires and analyzed using Structural Equation Modeling–Partial Least Square (SEM-PLS) to examine relationships among variables. Infrastructure quality and organizational culture significantly and positively affect organizational readiness. Clinical leadership, while not directly influencing readiness, has a significant positive effect on organizational culture. KRIS readiness at RS BM is shaped by infrastructure quality, clinical leadership, and organizational culture, with culture acting as the main mediator. The hospital shows strong readiness but still faces gaps in progress monitoring, inpatient room layout and privacy, leadership direction, and team participation. Strengthening monitoring mechanisms, improving room design according to KRIS standards, enhancing clinical leadership capacity, and fostering a collaborative organizational culture are essential to support comprehensive and sustainable KRIS implementation

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.

This study presents an ethical framework for learning analytics aimed at addressing key challenges related to the collection and use of student data in higher education. Learning analytics, a powerful tool for improving student outcomes and institutional decision-making, has raised ethical concerns regarding data privacy, transparency, fairness, and equity. The proposed framework integrates four core principles: data privacy, informed consent, transparency, and fairness, ensuring that institutions use learning analytics responsibly while safeguarding student rights. A central feature of the framework is its focus on promoting equitable decision-making, minimizing bias, and preventing the reinforcement of existing inequalities in algorithmic and data-driven decisions. The framework also emphasizes the importance of continuous ethical oversight, holding institutions accountable for ethical data use and adapting practices as technology evolves. The study concludes that the framework offers a comprehensive solution to the ethical challenges in learning analytics, providing institutions with a practical guide to embedding ethical principles in data practices. Additionally, the research discusses its potential to foster fairness, equity, and transparency in decision-making processes. Future research is recommended to refine the framework and explore its application across various educational contexts, ensuring responsible and inclusive use of learning analytics.

This paper reviews the role of Artificial Intelligence (AI) in Islamic education within secondary schools, emphasizing both its practical uses and the ethical challenges it presents. The review looks into the current trends, tools, and the impact of AI on the learning experience, as well as its ethical implications from an Islamic perspective. The study follows a systematic literature review (SLR) approach based on the PRISMA guidelines and includes research from 2022 to 2025, sourced from platforms like Google Scholar. After a thorough selection process, 15 articles were included in the review, offering valuable insights into the technological and ethical aspects of AI in Islamic secondary education. The use of AI has notably enhanced learning outcomes in Islamic education by allowing personalized learning, boosting student engagement, and streamlining feedback mechanisms. Tools like intelligent tutoring systems and educational chatbots have been widely adopted. However, challenges around data privacy, algorithmic bias, and technology access persist. Additionally, incorporating Islamic ethical values into AI-driven educational platforms presents both opportunities and challenges. Addressing these ethical implications is vital, requiring frameworks that align with Islamic principles such as maṣlaḥa (public welfare), justice, and human dignity. Education policies and teacher training programs should concentrate on promoting the responsible use of AI, ensuring it improves educational experiences while preserving ethical and cultural integrity.

The development of artificial intelligence has revolutionized higher education by increasing the efficiency and personalization of learning, but it has also posed a major challenge in the form of a decline in students' critical thinking skills due to their dependence on technology. Therefore, an in-depth philosophical study through the post-positivism, critical theory, and constructivism paradigms is needed to understand its impact on cognitive and epistemological processes. This study aims to analyze how these three paradigms guide the development of critical thinking in the context of AI. It uses a descriptive-analytical method based on secondary data from academic literature such as journals and books that have been critically synthesized. The findings show that post-positivism views AI as a tentative critical realism tool for empirical verification. Critical theory critiques power relations, ideology, and the ethics of technological domination. Constructivism emphasizes the construction of social knowledge through human-centered design that supports creativity and collaboration. The positive impacts of AI include instant feedback and content adaptation, while the negative impacts include social isolation, weak digital literacy, and data privacy. Therefore, the implication is the wise use of AI with the assistance of teaching staff to strengthen reflective analysis, ethical literacy, and paradigm adjustments in accordance with the exact or social sciences to create multidimensional learning in the digital era.

The integration of Artificial Intelligence (AI) in higher education has led to the increasing use of AI-powered adaptive learning models that support personalized and data-driven learning. However, studies examining preservice teachers’ perceptions of these models remain limited, despite their important role in future classroom implementation. This study aims to explore preservice teachers’ perceptions of AI-powered adaptive learning in higher education, focusing on perceived usefulness, learning adaptivity, learning experience, and perceived concerns. A descriptive qualitative research design was employed involving 53 preservice teachers from various universities. Data were collected using a Likert-scale questionnaire and open-ended questions. Quantitative data were analyzed descriptively using percentage distributions, while qualitative data were examined through simple thematic analysis. The findings reveal that preservice teachers generally demonstrate positive perceptions of AIpowered adaptive learning, particularly in terms of learning effectiveness, adaptability, and engagement. Nevertheless, concerns related to over-reliance on AI, ethical issues, and data privacy were also identified. These results indicate that preservice teachers show readiness to engage with AI-supported learning, while highlighting the need for teacher education programs to promote responsible and pedagogically informed AI integration.

Digital transformation has significantly reshaped human resource management (HRM) through the adoption of Human Resource Information Systems (HRIS), artificial intelligence (AI), big data analytics, e-learning platforms, and remote work technologies. Although these innovations improve efficiency and decision-making, they also generate ethical challenges related to data privacy, algorithmic bias, transparency, and employee monitoring. This article examines the role of professional ethics in HRM within the context of digital transformation, highlighting both emerging challenges and potential opportunities. This study employs a conceptual research approach supported by a comprehensive literature review of scholarly works on HRM, professional ethics, and digitalization. The analysis focuses on core ethical principles such as integrity, fairness, responsibility, professionalism, and confidentiality, and evaluates their implementation in digital HR practices. The findings indicate that unethical use of digital technologies may lead to discrimination, reduced employee trust, and violations of individual rights, particularly through biased AI-based recruitment systems and opaque performance evaluation mechanisms. However, digital transformation also offers opportunities to strengthen ethical HR governance. The use of ethical data management, algorithmic audits, digital transparency, and e-learning-based ethics training can enhance accountability and fairness in HR processes. The study concludes that integrating professional ethics with digital HRM is essential for developing human-centered, sustainable, and trustworthy organizations in the digital era.

The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is accelerating and reshaping how organizations attract, develop, manage, and retain talent. Despite abundant case examples and growing practitioner interest, academic findings remain fragmented regarding the antecedents (drivers), impediments (barriers), and organizational effects (outcomes) of AI-based HR transformation. This paper presents a PRISMA-guided systematic literature review of 112 peer-reviewed articles (2015–2025) to synthesize empirical and conceptual evidence on AI in HRM. Results identify three primary drivers: technological capability, strategic alignment, and a data-driven culture; three critical barriers: ethical concerns (bias, privacy, and transparency), skill and capability gaps, and resistance to change; and three outcome clusters: operational efficiency, enhanced employee experience, and elevated strategic HR contribution. We propose a socio-technical conceptual framework that models drivers moderated by barriers to outcomes, and we advance a research agenda focused on ethical governance, human–AI collaboration, capability measurement, and longitudinal evaluation. The review contributes to theory by integrating socio-technical and dynamic capability  perspectives and provides actionable guidance for HR leaders on responsible AI adoption.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

The rapid rise of Indonesia’s digital economy is changing how people participate as citizens and how their consumer rights are protected. This study explores the legal dimensions of digital citizenship through a normative juridical review of the Consumer Protection Law, the Electronic Information and Transactions Law, and the Personal Data Protection Law. Although these laws provide basic safeguards, significant problems remain such as data privacy violations, unclear algorithmic practices, cross-border legal barriers, and deceptive online advertising. Low public legal awareness and the absence of a unified dispute-resolution system further leave consumers vulnerable to powerful global digital platforms. The study argues that stronger protection requires embedding digital citizenship principles into Indonesia’s legal framework, harmonizing national rules with international standards, enhancing platform oversight, and improving digital literacy. In conclusion, a flexible and forward-looking legal approach is crucial to making consumer protection a core part of digital citizenship and ensuring public trust and human rights in the digital era.

This study aims to analyze user sentiment toward the integration of Artificial Intelligence (AI) in online learning platforms, which are increasingly expanding in the digital era. With the growing use of AI technologies in education—such as learning chatbots, material recommendation systems, and automated assessments—it is essential to understand users’ perceptions and reactions to these implementations. The research employs sentiment analysis based on text mining using user review data collected from various online learning platforms. The analysis process includes data preprocessing, sentiment classification using machine learning algorithms, and interpretation of results based on the proportion of positive, negative, and neutral sentiments. The findings indicate that most users express positive sentiments toward AI integration, as it enhances learning efficiency and personalization. However, some users raise concerns regarding data privacy and the lack of human interaction. This study is expected to serve as a reference for educational platform developers to design AI systems that are more adaptive, transparent, and user-centered