The rapid adoption of artificial intelligence (AI) has significantly transformed various sectors, such as healthcare, finance, and transportation. However, it also raises critical challenges regarding data privacy, particularly in large-scale data collection and processing. This study explores the differences and similarities in data privacy regulations governing AI between Europe and Asia, focusing on the General Data Protection Regulation (GDPR) in Europe and various regulations such as the Act on the Protection of Personal Information (APPI) in Japan and the Personal Information Protection Law (PIPL) in China. Using a qualitative approach with comparative legal analysis, this research evaluates the principles, flexibility, and practical implications of these regulations for fostering responsible AI development. The findings reveal that while GDPR emphasizes individual protection through transparency and explicit consent, Asia adopts a more flexible approach tailored to national needs, balancing innovation and privacy. However, challenges such as harmonizing cross-border data policies and adapting regulations to rapidly evolving technologies persist. This study contributes to the discourse by highlighting the implications of these regulatory differences for global cooperation and offering strategic recommendations for policymakers and industries. In a globalized digital landscape, aligning legal frameworks is essential not only to protect individual rights but also to build public trust in emerging AI technologies.

Protecting personal data has become increasingly critical in the digital era, especially in Indonesia, where data breaches remain a pressing issue. Blockchain technology has emerged as a potential solution due to its decentralization and enhanced security mechanisms. This study aims to analyze the legal aspects of implementing blockchain technology for personal data protection in Indonesia, focusing on its effectiveness and alignment with existing regulations. A qualitative methodology was employed, including case studies in the fintech and healthcare sectors, supported by normative legal analysis. The findings indicate that blockchain significantly improves data security, with companies reporting a notable decrease in data breaches after adoption. Mechanisms such as hashing and decentralization ensure data integrity and reduce unauthorized access. However, regulatory gaps and high implementation costs hinder widespread adoption. The Personal Data Protection Law (UU PDP) in Indonesia does not yet address the unique characteristics of blockchain, creating legal ambiguities. This study highlights the need for regulatory updates and suggests targeted incentives to encourage adoption. The research contributes to theoretical understanding by integrating legal analysis with blockchain implementation and offers practical recommendations for policymakers. Future studies should explore cross-sectoral applications and comparative analyses with countries successfully adopting blockchain.

The death penalty remains one of the most contentious policies in addressing narcotics crimes in Indonesia. While implemented as a stringent measure to combat drug trafficking, the policy often sparks debates regarding its effectiveness and implications for human rights. This study aims to analyze public perceptions of the death penalty policy for narcotics-related offenses, focusing on how demographic factors such as age, education, and residence influence public support. Using a quantitative approach, this research employed a nationwide survey involving 1,500 respondents selected through stratified random sampling. Data collection combined online and offline methods to ensure broad representation. The findings reveal significant demographic variations in support for the death penalty. Younger and middle-aged respondents (25–40 years old) showed the highest levels of support (70%), particularly in urban areas (70%) compared to rural areas (55%). Educational attainment also influenced perspectives, with higher education levels correlating with increased support for the policy. The study concludes that public perceptions of the death penalty are shaped significantly by demographic and socio-cultural factors. These findings underscore the need for targeted communication strategies to address varying public concerns, particularly in rural areas where access to information may be limited. The research contributes to policy discussions by providing empirical insights into public opinion on this controversial issue. It recommends integrating public education campaigns and alternative rehabilitative measures to enhance public understanding and acceptance of narcotics policies. By grounding policy development in evidence-based research, this study aims to support more equitable and effective approaches to addressing drug-related crimes in Indonesia

The digital era has significantly increased the complexity of cross-border crimes, particularly cybercrime, which poses substantial challenges for countries, including Indonesia. With a reported rise in cross-border cybercrime incidents of over 50% in the last five years, Indonesia faces critical legal and technical hurdles in combating such threats. This study explores the rights and obligations of states in addressing cross-border cybercrime, focusing on comparing Indonesia's legal framework with international standards, specifically the Budapest Convention. Employing a normative analytical method, this research examines international and national legal documents to identify gaps and evaluate the alignment of Indonesia's cybercrime regulations with global standards. Key findings reveal that Indonesia's cybercrime policies lack mandatory international cooperation mechanisms and are limited to national jurisdiction, which restricts the country’s ability to effectively address cybercrimes involving foreign perpetrators. In contrast, the Budapest Convention emphasizes structured international collaboration, robust privacy protections, and flexible jurisdictional arrangements, providing a comprehensive framework for managing transnational cyber threats. The study concludes that harmonizing Indonesia’s regulations with international standards, such as the Budapest Convention, is essential for improving the nation's capacity to combat cross-border cybercrime. Recommendations include enhancing legal frameworks to mandate international cooperation, establishing specialized units within law enforcement agencies equipped with advanced digital forensic tools, and strengthening privacy protections to align with global norms. This research contributes to the discourse on international cybercrime management by offering practical strategies to bridge regulatory gaps and bolster Indonesia’s position in global cybersecurity collaborations. The findings underscore the urgency for policy reform to address the evolving challenges of digital threats in an interconnected world.

Human Computer Interaction (HCI), a growing research field in science and engineering, aims to provide a natural way for humans to use computers as tools. Humans prefer to interact with each other mainly through speech, but also through facial expressions and gestures, for certain parts of the speech and displays of emotions. The identity, age, gender, and emotional state of a person can be obtained from his face. The impression we receive from the expression reflected on the face affects our interpretation of the spoken word and even our attitude towards the speaker himself. Although emotion recognition is an easy task for humans, it still proves to be a difficult task for computers to recognize user`s emotional state. Advances in this area promise to arm our technological environment by means for more effective interactions with humans, and hopefully the impact of facial expressions on cognition will increase rapidly in the future. Will do. In recent years, the adoption of digital has increased rapidly, and the quality has improved significantly. Digital printing has resulted in fast delivery and needs-based costs. This article describes a sophisticated combination classifier approach, an empirical study of ensembles, stacking, and voting. These three approaches were tested on Nave Bayes (NB), Kernel Naive Bayes (kNB), Neural Network (NN), Auto MultiLayer Perceptron (Auto MLP), and Decision Tree (DT), respectively. The main contribution of this paper is the improvement of the classification accuracy of facial expression recognition tasks. In both persondependent and nonpersondependent experiments we showed that using a combination of these classifier combinations gave significantly better results than using individual classifiers. It has been observed from experiments that the overall voting technique by voting achieves the best classification accuracy.

Human Computer Interaction (HCI), a growing research field in science and engineering, aims to provide a natural way for humans to use computers as tools. Humans prefer to interact with each other mainly through speech, but also through facial expressions and gestures, for certain parts of the speech and displays of emotions. The identity, age, gender, and emotional state of a person can be obtained from his face. The impression we receive from the expression reflected on the face affects our interpretation of the spoken word and even our attitude towards the speaker himself. Although emotion recognition is an easy task for humans, it still proves to be a difficult task for computers to recognize user`s emotional state. Advances in this area promise to arm our technological environment by means for more effective interactions with humans, and hopefully the impact of facial expressions on cognition will increase rapidly in the future. Will do. In recent years, the adoption of digital has increased rapidly, and the quality has improved significantly. Digital printing has resulted in fast delivery and needs-based costs. This article describes a sophisticated combination classifier approach, an empirical study of ensembles, stacking, and voting. These three approaches were tested on Nave Bayes (NB), Kernel Naive Bayes (kNB), Neural Network (NN), Auto MultiLayer Perceptron (Auto MLP), and Decision Tree (DT), respectively. The main contribution of this paper is the improvement of the classification accuracy of facial expression recognition tasks. In both persondependent and nonpersondependent experiments we showed that using a combination of these classifier combinations gave significantly better results than using individual classifiers. It has been observed from experiments that the overall voting technique by voting achieves the best classification accuracy.