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International criminal law is a crucial instrument within the international legal system designed to address serious crimes that transcend national jurisdiction, such as genocide, war crimes, and crimes against humanity. International criminal law serves as a means to uphold justice, maintain international order, and prevent impunity for perpetrators of serious crimes that have a broad impact on the global community. This study aims to examine the enforcement of international criminal law in the modern era, emphasizing law enforcement mechanisms and the role of cooperation between states and international institutions. The research method used is normative juridical research, with an approach to relevant laws and regulations, legal doctrine, and court decisions. The results indicate that the effectiveness of international criminal law enforcement still faces various obstacles, primarily stemming from state political interests, weak commitment to international cooperation, and inconsistencies between legal norms and their implementation. The principle of complementarity is a fundamental element of this system, as it positions states as the primary actors in the prosecution process, while international judicial institutions play a complementary role if national mechanisms are ineffective. Furthermore, harmonization of national laws with international criminal law provisions and strengthening cross-border cooperation are determining factors in the success of law enforcement. This study concludes that synergy between states and international institutions, accompanied by strong political commitment, is a key prerequisite for the realization of a just and sustainable international criminal law enforcement system.

The rapid development of digital technology has significantly transformed financial transactions in Indonesia, particularly through the growing use of e-wallets as practical and efficient payment tools. In a country with a Muslim-majority population, ensuring that e-wallet services comply with Islamic principles—free from riba, gharar, and maysir—is essential. This study aims to analyze Indonesia’s legal politics in regulating the use of e-wallets within the Islamic financial system and to assess their alignment with sharia principles. This research employs a normative juridical method with a qualitative descriptive approach by examining laws, regulations, and fatwas related to sharia-based fintech. The findings indicate that the Indonesian regulatory framework—through the OJK, Bank Indonesia, and DSN-MUI—has attempted to harmonize policies to support sharia-compliant digital financial services. However, several challenges remain, including limited e-wallet platforms with sharia certification, low digital sharia literacy among users, and the absence of detailed technical regulations specific to sharia e-wallet operations. This study recommends strengthening regulatory guidelines, increasing public literacy, and enhancing collaboration between regulators and the fintech industry to promote the development of sharia-compliant e-wallets that are secure, innovative, and aligned with Islamic financial principles.

The integration of Information and Communication Technology (ICT) and tools like Canva has revolutionized English Language Teaching (ELT) in Indonesia, shifting from traditional methods to interactive, student-centered approaches. This study aims to explore the benefits and challenges of incorporating these technologies into ELT practices. Employing a qualitative literature review methodology, the research analyzed over 20 empirical studies from 2020 to 2023 on Canva and ICT in Indonesian ELT, supplemented by global Computer-Assisted Language Learning (CALL) frameworks. Key findings reveal that ICT and Canva enhance student motivation, creativity, and communicative skills through multimodal and constructivist learning, while fostering authentic language exposure and collaboration. However, challenges include inadequate infrastructure, limited teacher training, digital inequities between urban and rural areas, ethical issues such as copyright infringement, and increased teacher workload. The implications underscore the need for systematic professional development, institutional support, curriculum alignment, and policies promoting equitable access to ensure transformative and sustainable technology integration in Indonesian ELT.  

The UNAMA Library employs information systems to enhance its academic services. Nevertheless, its administrative framework continues to encounter obstacles, such as inadequate service system support for users, constrained resources allocated for the management and upkeep of the system, an absence of standardized protocols for addressing technical challenges, and insufficient assessment system efficacy. If these issues remain unaddressed, the operational effectiveness of the library information system will be compromised, thereby thwarting the objective of delivering dependable information services. This investigation seeks to illuminate the maturity level of information system governance as delineated by COBIT 2019 within the Decision Support Systems (DSS) domain, specifically focusing on the DSS01 (Manage Operations) and DSS02 (Manage Service Requests and Incidents) processes. The findings suggest that the degree of information system governance capability the UNAMA Library is situated at the Established Process level (level 3), signifying that the process has undergone implementation; however, it has yet to be comprehensively documented and consistently evaluated. Moreover, a disparity persists between the existing state and the anticipated capability level of the organization, particularly concerning IT operations management, the standardization incident handling, and the documentation of operational procedures. An elucidation of the expected level is articulated, especially in terms of operational standards, incident documentation, and IT infrastructure oversight. Recommendations encompass the formulation of standard operating procedures (SOPs), the enhancement of documentation practices, and periodic assessments grounded in COBIT 2019. These findings are anticipated to assist libraries in augmenting the efficacy of information systems governance and the quality of IT services.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The integration of Artificial Intelligence (AI) into educational settings, particularly in formative assessments, offers significant benefits in terms of personalized learning, real time feedback, and increased efficiency. However, the successful implementation of AI driven formative assessments depends not only on technological capabilities but also on socio cultural and organizational factors that shape its adoption. This study explores the socio technical factors influencing the use of AI in formative assessments, emphasizing the importance of considering cultural diversity, institutional culture, and educators' beliefs. AI technologies, while powerful in automating grading and providing personalized assessments, often face limitations in addressing complex student responses that require human judgment. Furthermore, cultural factors, such as students' prior exposure to technology and different cultural attitudes towards AI, play a critical role in the acceptance and effectiveness of these tools. Organizational factors, including leadership support, digital literacy, and the readiness of institutions to adopt AI, are also key determinants in the successful implementation of AI systems in education. Teachers’ beliefs about assessment influence their acceptance and use of AI tools, highlighting the need for professional development and training to ensure that AI enhances pedagogical goals rather than replacing human expertise. The study concludes that the alignment of technology, culture, and assessment beliefs is essential for the effective use of AI driven formative assessments in educational settings. Recommendations for educational institutions include adopting a socio technical approach to AI integration, with a focus on providing resources, training, and fostering a culture of innovation. Future research directions should focus on expanding studies to diverse educational contexts, conducting longitudinal research on AI’s impact on learning outcomes, and exploring additional socio technical frameworks to guide AI adoption in education.

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.

This study aims to analyze and synthesize research findings related to the integration of Buddhist values in the development of interactive learning media. The research employed a qualitative approach using a systematic literature review method on fifty selected articles published between 2018 and 2025. Data were collected through a structured process of identification, screening, and extraction from primary sources consisting of accredited national journals and reputable international journals. The data were analyzed using thematic and comparative synthesis techniques to identify patterns, effectiveness, and research gaps. The results show that interactive learning media based on Buddhist values can enhance students’ motivation, moral understanding, and engagement through the use of technologies such as gamification, educational animation, augmented reality, and mobile applications. The effectiveness of these media is strongly influenced by the alignment between Buddhist ethical principles, instructional design, and the cultural context of learning. Furthermore, the study reveals that the successful integration of spiritual values into digital media depends on educators’ readiness, digital literacy, and technological infrastructure support.

This study aims to examine the use of artificial intelligence (AI) in transforming Buddhist Sunday School education at the Shimakalingga Temple in Jepara in the digital era. Using a qualitative descriptive approach, data were collected through observation, interviews, and documentation studies. The results show that AI technology is beginning to be utilized in the learning process, such as the use of interactive visual media, Dharma quiz applications, and digital platforms for distance learning. The use of AI has been proven to increase student interest and understanding of Buddhist teachings, as well as support more inclusive and adaptive learning. However, challenges remain, including limited resources, lack of teacher training, and the need for ethical guidelines for the use of AI in the context of religious education. This study concludes that AI plays a significant role in supporting the transformation of Buddhist education, but its use must remain balanced with spiritual values and the role of teachers as moral guides. Therefore, collaboration between the temple, educators, and technology is needed to create a sustainable learning system based on Dharma values.

Literacy skills in elementary schools play a crucial role in supporting students’ academic development and overall learning success. However, observations conducted at SDN 2 Demulih revealed that many students still experience difficulties in comprehending reading materials and retelling stories coherently and systematically. This study aims to enhance students’ literacy and language skills through the implementation of the retelling technique as an instructional strategy. A descriptive research approach employing the ADDIE development model was applied, consisting of five stages: analysis, design, development, implementation, and evaluation. The program involved 21 students from grades IV–VI and utilized two audiovisual story media, namely “Independence” and “Bawang dan Kesuna.” The results indicate that the application of the retelling technique based on audiovisual media effectively improved students’ reading comprehension, learning activeness, and self-confidence in speaking activities. Students were able to reconstruct storylines accurately and express moral messages using their own words. Therefore, the retelling technique is proven to be an engaging, practical, and effective literacy learning strategy for elementary school students.

The development of digital information technology has fundamentally transformed the ways university students access and interpret religious information. Social media platforms such as TikTok, Instagram, and YouTube have become primary spaces where students encounter diverse forms of religious content, ranging from educational dakwah to provocative narratives that may trigger tension and conflict. This phenomenon creates both opportunities and challenges for shaping religious moderation, particularly among first-semester students who are in the formative stage of constructing their intellectual and religious identities. This study aims to explore in depth the dynamics of religious moderation among first-semester students at IAINU Tuban within the context of digital polarization. A descriptive qualitative method was employed, utilizing semi-structured interviews, observations, and documentation as data collection techniques. Data were analyzed through thematic processes of reduction, presentation, and conclusion drawing. The findings reveal that students exhibit high social media engagement and are frequently exposed to short dakwah content circulated through algorithmic mechanisms. However, they also confront digital polarization characterized by emotional disputes, informational bias, and negative perceptions of other religious groups. While students demonstrate an initial understanding of religious moderation—such as wisdom, tolerance, and resistance to provocation—they have not fully developed adequate religious digital literacy to critically evaluate extreme content. This research concludes that social media simultaneously serves as a medium for dakwah and a site of polarization, highlighting the need for systematic reinforcement of digital literacy and religious moderation within Islamic higher education. 

The rapid development of e-commerce as part of the broader digital transformation has introduced various new forms of transactions that were not explicitly recognized in classical Islamic jurisprudence. These include digital contracts, non-physical goods, cloud-based services, and electronic payment systems. Such innovations present a range of legal challenges, particularly concerning the validity of contracts, the legal status of digital products, the permissibility and security of electronic payment methods, and the protection of consumers in online transactions. To address these challenges, Islamic scholars have increasingly turned to ijtihad the process of independent reasoning and interpretation—as a methodology capable of providing legal solutions for contemporary issues. This study employs a qualitative approach, primarily through literature review, to examine the foundational principles of ijtihad, various methodological tools, and their applicability to different aspects of e-commerce transactions. The findings demonstrate that ijtihad methods, including qiyās (analogical reasoning), istislāh (consideration of public interest), istihsān (juridical preference), and ‘urf (customary practice), enable Islamic law to remain adaptive and flexible, while still oriented toward justice and public welfare. By applying these methods, Islamic jurisprudence can provide legal certainty, uphold fairness, and ensure that e-commerce practices align with Shariah principles, ultimately fostering trust and ethical behavior in the digital marketplace.

The development of the Industrial Revolution 4.0 to the Society 5.0 era presents new challenges for education, including Islamic Religious Education (PAI). PAI is required not only to transfer religious knowledge but also to shape character, morality, and 21st-century competencies. This study aims to examine the philosophical foundations, characteristics, and application of PAI learning models to ensure their relevance in modern education. The research employed a qualitative approach with a library research design, involving the exploration of primary and secondary literature, critical reading, systematic note-taking, and content analysis validated through triangulation of classical and contemporary sources. The findings indicate that PAI learning models are conceptual, theoretical, systematic, and rooted in religious, philosophical, psychological, and sociological foundations. Four main models behavioristic, information processing, social, and humanistic each have strengths and limitations, yet their integration is relevant to forming insan kamil, a holistic human being balanced in cognitive, affective, psychomotor, spiritual, and social aspects. Therefore, developing PAI learning models based on Islamic philosophy and adaptive to technological advancements is a strategic necessity to face global challenges.

In this study, we will discuss the concept of Islamic education from Buya Hamka's perspective. Buya Hamka was an Indonesian Muslim cleric and scholar with a profound perspective on the relationship between knowledge, faith, and morality. The purpose of this study is to analyze aspects of Islamic education according to Buya Hamka and their relevance to the modern education system. This research was conducted using a qualitative descriptive approach, drawing on relevant academic literature such as journals, books, and articles. This research concludes that Buya Hamka views Islamic education as an effort to shape a holistic human being, balancing spiritual, intellectual, moral, and social aspects. Hamka emphasized that education is not only intended to cultivate the intellect but also to purify the heart and develop noble character. According to Hamka, Islamic education must be grounded in monotheism, direct educators as role models of morality, and integrate religious and intellectual knowledge for the benefit of the community. Buya Hamka's perspective is sufficient to address the challenges of modern education, which tends to focus on academic values ​​without considering the instilling of moral and spiritual values.

This study analyzes the implementation of Islamic inheritance law (mawaris) in contemporary Muslim society as a means to realize justice and blessing (barakah) in the distribution of inherited wealth. The research is motivated by the growing number of inheritance disputes caused by limited understanding of faraid principles, the persistence of customary inheritance practices, and the coexistence of national legal systems that often diverge from Islamic inheritance regulations. These conditions frequently lead to unequal distribution and family conflicts. The study aims to examine the relevance of mawaris in modern contexts and to explore how its application, when aligned with the objectives of Islamic law (maqasid al-shariah), can address current social challenges. Employing a qualitative method with a normative library research approach, the study draws on primary sources such as the Qur’an, Hadith, and classical fiqh texts, as well as secondary sources including contemporary books and scholarly journal articles. Data were analyzed through content analysis to identify key concepts and arguments regarding the contemporary application of mawaris. The findings show that proper implementation of Islamic inheritance law fosters justice, minimizes family disputes, strengthens kinship ties, and safeguards the blessing of wealth. Nevertheless, insufficient public literacy on Islamic inheritance law and the dominance of non-sharia considerations remain significant barriers. Therefore, the study underscores the need for enhanced public education, greater involvement of religious scholars in inheritance mediation, and contextual integration of Islamic legal principles to ensure fair and beneficial inheritance distribution in modern Muslim families.

The professional competence of Islamic Education (PAI) teachers plays a decisive role in determining the quality of learning at the elementary school level. In practice, many teachers still encounter challenges in mastering subject content, developing systematic teaching materials, applying innovative learning strategies, and integrating educational technology into classroom activities. These conditions indicate the need for structured professional development efforts through collaborative forums. This study aims to analyze the role of the Islamic Education Teachers Working Group (KKG PAI) in maximizing the professional competence of PAI teachers in public elementary schools in Panti District, Pasaman Regency, and to identify the supporting and inhibiting factors influencing its implementation. This research employed a qualitative approach with a case study design. Data were collected through in-depth interviews, participatory observation, and documentation involving the KKG supervisor, the head of KKG PAI, and member teachers. Data analysis was conducted using an interactive model consisting of data reduction, data display, and conclusion drawing. The findings reveal that KKG PAI functions as a strategic platform for collaborative lesson planning, subject-matter enrichment, peer reflection, and technology-based instructional training. Supporting factors include strong teacher commitment and collaborative culture, while limitations in facilities and technological disparities remain challenges. Strengthening the sustainability and management of KKG programs is essential to enhance teachers’ professional competence and improve the overall quality of Islamic education learning.

Digital technology has become an essential part of managing small and medium-sized enterprises (SMEs), especially in efforts to achieve business sustainability. Kampong Bharu, as an iconic area in Kuala Lumpur, is home to numerous micro, small, and medium-sized enterprises (MSMEs) operating in the culinary, craft, and tourism sectors, but faces challenges in implementing environmentally friendly practices. This research aims to analyze the application of green digital management to MSMEs in Kampong Bharu as a sustainable business strategy. The research method uses a qualitative approach with in-depth interviews and participant observation of 15 MSME actors. The research results show that integrating digital technology with environmentally friendly principles, such as paperless digital marketing, energy-efficient use, and sustainable packaging systems, can enhance the competitiveness and positive image transformation of MSMEs. This finding has practical implications for business owners and stakeholders in developing a digital-based green MSME model in historic urban areas. These findings can also serve as a guide for implementing sustainable business strategies in the MSME sector in other major cities.