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Education in the current digital era demands the use of smart technology and social media platforms in the learning process to suit the character of the digital generation. However, the competence of lecturers in Indonesia in this regard is still limited. Therefore, the workshop "Revolutionary Strategies for Modern Learning Through the Integration of Smart Technology and Social Media Platforms for Digital Generation Education" is important to implement. This workshop aims to provide understanding and skills to 187 lecturers in implementing technology and social media for learning innovation for digital generation students. Workshop material includes smart technology trends, use of social media platforms, digital evaluation and assessment strategies, as well as discussion of challenges and opportunities for integrating technology and social media in digital era learning. Participants' enthusiasm was quite high and the evaluation results showed that 91% were interested in implementing modern learning strategies after the workshop.

The rapidly evolving landscape of cryptanalysis necessitates an urgent and detailed exploration of the high-degree non-linear functions that govern the relationships between plaintext, key, and encrypted text. Historically, the complexity of these functions has posed formidable challenges to cryptanalysis. However, the advent of deep learning, supported by advanced computational resources, has revolutionized the potential for analyzing encrypted data in its raw form. This is a crucial development, given that the core principle of cryptosystem design is to eliminate discernible patterns, thereby necessitating the analysis of unprocessed encrypted data. Despite its critical importance, the integration of machine learning, and specifically deep learning, into cryptanalysis has been relatively unexplored. Deep learning algorithms stand out from traditional machine learning approaches by directly processing raw data, thus eliminating the need for predefined feature selection or extraction. This research underscores the transformative role of neural networks in aiding cryptanalysts in pinpointing vulnerabilities in ciphers by training these networks with data that accentuates inherent weaknesses alongside corresponding encryption keys. Our study represents an investigation into the feasibility and effectiveness of employing machine learning, deep learning, and innovative random optimization techniques in cryptanalysis. Furthermore, it provides a comprehensive overview of the state-of-the-art advancements in this field over the past few years. The findings of this research are not only pivotal for the field of cryptanalysis but also hold significant implications for the broader realm of data security.

Digital literacy has become a key foundation for educational innovation and economic participation in the 21st century. As global economies transition toward digitalization, integrating digital competencies into senior high school curricula is increasingly viewed as essential for supporting Sustainable Development Goal 8 (SDG 8), which emphasizes decent work and inclusive economic growth. This study investigates the role of public policy in shaping digital literacy education in senior high schools through a comparative case study of Indonesia, the Philippines, and Canada. Employing qualitative policy analysis, the research explores how each country’s policy framework conceptualizes, implements, and evaluates digital literacy initiatives. The analysis focuses on curriculum integration, resource allocation, and stakeholder involvement, while also examining how these programs contribute to employability and economic resilience. The findings reveal notable differences in policy design and institutional commitment, with Canada demonstrating a more systematic integration of digital literacy, the Philippines emphasizing access and equity, and Indonesia facing challenges related to resource disparities and curriculum consistency. These variations illustrate how national contexts influence the inclusiveness and effectiveness of digital literacy policies. The study concludes with recommendations for policymakers to design context-sensitive, equitable, and future-oriented digital literacy strategies that align with labor market demands and promote sustainable economic growth.

Local wisdom-based character education has a strategic role in forming a generation with integrity, strong personality, and high moral awareness. This study aims to analyze the implementation of local wisdom-based character education in learning and identify effective strategies and obstacles faced in its implementation. Using a qualitative approach, data were collected through interviews, observations, and documentation studies. The results of the study indicate that the integration of local wisdom values ​​in education can increase cultural awareness, strengthen national identity, and form positive characters in students. However, the implementation of local wisdom-based character education still faces challenges, such as lack of understanding of educators, minimal policy support, the influence of globalization, and limited resources. Therefore, synergy is needed between schools, families, communities, and the government so that local wisdom-based character education can run effectively and sustainably.    

Artificial Intelligence (AI)-based Decision Support Systems (DSS) have become a central component of digital transformation initiatives across various industries. While prior studies have primarily emphasized technical aspects such as accuracy, performance, and computational efficiency, less attention has been given to the integration of human-centered principles and scalable architectural design. This study aims to examine how AI-based DSS can be enhanced through the combined application of Human-Centered Artificial Intelligence (HCAI) principles and scalable AI architecture. Using a qualitative, literature-based research methodology, this study systematically analyzes peer-reviewed publications indexed in Scopus to identify key dimensions influencing the effectiveness and sustainability of AI-driven DSS. The findings indicate that technical capabilities alone are insufficient to ensure successful adoption and long term impact. Instead, transparency, explainability, ethical governance, and user empowerment core elements of HCAI are critical for fostering trust and user acceptance. Furthermore, scalable architectural principles, including modularity, interoperability, and adaptability, are essential for enabling AI-based DSS to operate reliably in large-scale and dynamic environments. This study contributes a unified conceptual framework that bridges technical scalability and human-centered design, offering theoretical insights and practical guidance for developing trustworthy, scalable, and sustainable AI-based Decision Support Systems in digital transformation contexts.

The rapid growth of cross-platform applications has significantly increased the volume and diversity of sensitive user data processed across heterogeneous and distributed environments. Personally identifiable information, device identifiers, behavioral data, and financial information are routinely collected to support personalization, analytics, and service optimization. While these practices enhance application functionality and user experience, they also introduce substantial privacy risks, including unauthorized data access, device fingerprint–based re-identification, cross-user data leakage, and large-scale data breaches. These risks are further amplified by distributed processing architectures and extensive third-party library integrations commonly used in modern cross-platform systems. This study aims to systematically analyze privacy issues in cross-platform applications by examining the types of sensitive data involved, identifying dominant privacy threats, and reviewing state-of-the-art privacy-preserving mitigation strategies. A systematic literature-based methodology was employed, focusing on recent Scopus-indexed journal articles, conference papers, and book chapters. The analysis synthesizes findings using thematic categorization and a conceptual research framework that maps sensitive data sources to privacy threats and corresponding mitigation mechanisms. The results indicate that privacy risks in cross-platform applications originate not only from external attacks but also from internal architectural weaknesses, such as flawed authorization logic and excessive data sharing across system components. Privacy-preserving techniques including differential privacy, federated learning, blockchain-based data governance, secure multi-party computation, and fine-grained access control mechanisms are shown to provide stronger privacy guarantees compared to conventional centralized approaches. However, these techniques also present trade-offs related to system complexity and performance. Overall, the study highlights the importance of adopting a multi-layered, privacy-by-design approach to ensure sustainable, trustworthy, and regulation-compliant cross-platform application development.

This study examines the strengthening of regional disaster management governance through a risk-based policy framework, focusing on the role of local governments in disaster-prone areas. The object of the study is the disaster management system implemented by the Dumai City Regional Disaster Management Agency (BPBD), which faces various potential natural and non-natural disasters. Although national and regional regulatory frameworks are in place, disaster management practices at the regional level still tend to be reactive, partial, and not fully based on comprehensive risk analysis. Therefore, this study aims to identify key issues in regional disaster management governance and analyze the role of risk-based policies in strengthening institutional effectiveness, cross-sectoral coordination, and disaster preparedness. This study uses a qualitative descriptive approach with data collection techniques through in-depth interviews with BPBD officials, relevant stakeholders, and community representatives in disaster-prone areas, supported by field observations and documentation studies of policies, disaster risk assessment documents, and institutional reports. Data analysis was conducted using thematic analysis methods to identify patterns related to governance structures, policy implementation, and disaster risk management practices. The study results indicate that disaster management governance in Dumai City has shown progress in regulatory aspects and emergency response capacity, but there are still weaknesses in the integration of risk into development planning, inter-agency coordination, community participation, and mitigation and prevention efforts. This study concludes that the implementation of a risk-based policy framework can encourage more anticipatory, integrated, and sustainable disaster management governance, and provides important implications for the formulation of public policy at the regional level.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

The integration of Artificial Intelligence (AI) chatbots into customer service operations within digital banks has become a transformative trend in the banking sector, particularly in Indonesia. This study examines the effectiveness of AI chatbots in improving both operational efficiency and customer satisfaction in three selected Indonesian digital banks. The research focuses on evaluating the impact of AI chatbots in reducing response times, handling high volumes of customer inquiries, and improving service availability. The study also delves into customer feedback, measuring satisfaction with the ease of use, personalization, and responsiveness of the AI systems. The results demonstrate that AI chatbots significantly enhance customer service efficiency by reducing wait times and automating routine inquiries, which reduces the need for human intervention. Additionally, customers reported high satisfaction with the round-the-clock availability of AI chatbots and the personalized financial advice provided. However, despite these benefits, challenges related to data security, chatbot accuracy, and customer adaptation to automated systems remain prevalent. Data privacy concerns and issues regarding the chatbot's ability to handle complex queries were noted as limiting factors in customer trust and satisfaction. The study highlights the implications of these findings for the future of digital banking in Indonesia, emphasizing the need for improved chatbot integration, stronger data security measures, and enhanced user experience design. The integration of AI in customer service operations holds significant potential for digital banks and the broader financial sector to improve operational efficiency, reduce costs, and meet the evolving expectations of customers.

Augmented Reality (AR) technology has emerged as a transformative tool in the e-commerce industry, enhancing the online shopping experience by offering interactive and immersive features. This study explores the impact of AR on consumer behavior, focusing on key factors such as trust, satisfaction, purchase decisions, and customer loyalty. Through a user experiment, participants interacted with AR-integrated e-commerce platforms, testing features such as virtual try-ons and product visualizations in real-world contexts. The results revealed that AR significantly enhances consumer trust, with more realistic and accurate product visualizations increasing confidence in both the product and platform. Customer satisfaction was also notably higher after interacting with AR features, with users reporting a more engaging and enjoyable shopping experience. Furthermore, purchase intention was positively influenced by the ability to visualize products and receive real-time, interactive feedback. Repeated exposure to AR led to increased customer loyalty, as participants expressed a higher likelihood of returning to use AR tools in future shopping experiences. These findings suggest that AR provides a strong competitive advantage for e-commerce platforms, enabling them to differentiate themselves in a crowded market by offering personalized, engaging, and trust-building shopping experiences. This research also highlights the practical implications for e-commerce businesses, recommending the integration of user-friendly and interactive AR features to enhance engagement, satisfaction, and decision-making. Future research should explore the broader applications of AR across industries and consumer segments to further understand its potential to revolutionize online shopping.

This study explores the development of low-carbon concrete by integrating industrial waste materials and CO₂-absorbing minerals to reduce carbon emissions in the construction industry. The research investigates various mix ratios involving fly ash, slag, and CO₂-absorbing minerals, aiming to optimize both performance and sustainability. Experimental methods included compressive strength testing, carbon emission measurement, and durability evaluation. The results indicate that low-carbon concrete formulations achieve up to 45% reduction in carbon emissions compared to conventional concrete, which uses Portland cement. Despite a slight decrease in compressive strength, the optimal mix reached 34 MPa, which is sufficient for structural applications. Durability testing revealed that concrete with CO₂-absorbing minerals showed improved long-term performance compared to other formulations. The findings highlight the potential of using industrial waste materials and carbon capture technologies to create more environmentally sustainable concrete while maintaining necessary structural properties. This study contributes to the growing demand for eco-friendly construction materials and supports the implementation of low-carbon concrete in large-scale industrial applications. Further optimization of mix ratios and long-term performance studies are recommended for broader adoption in the construction industry.

Renewable energy microgrids integrated with smart control systems are emerging as a sustainable solution for electrifying rural industrial zones, offering substantial improvements in energy efficiency and reductions in carbon emissions. This study explores the implementation of hybrid renewable energy systems, combining solar and wind energy, and the integration of Internet of Things (IoT) sensors to optimize energy consumption in real-time. The findings highlight that the combination of solar and wind energy in microgrids leads to up to a 30% increase in energy efficiency, with a significant reduction in CO₂ emissions, reaching up to 50% compared to traditional grid systems. IoT sensors play a crucial role in load forecasting, optimization, and system stability, enabling real-time monitoring and proactive adjustments to energy distribution. Additionally, the implementation of these systems in rural industrial zones not only provides reliable, clean energy but also reduces reliance on fossil fuels, making them economically viable and environmentally sustainable. However, challenges such as high initial investment costs, integration complexities, and the need for skilled technicians remain. Despite these barriers, the long-term benefits of reduced energy costs, improved energy security, and lower carbon footprints make renewable energy microgrids a promising solution. The study suggests that these systems can be scaled to other rural regions facing similar challenges in energy access and carbon emissions, offering a path to sustainable development. Further research is recommended to explore alternative renewable energy combinations and advancements in IoT applications to improve system scalability and efficiency.

This qualitative study investigates the challenges faced by new engine officers in maritime settings, focusing on their transition from theoretical learning to practical engine maintenance. Through semi-structured interviews with experts in marine engineering and engine management, the research identifies significant gaps between theoretical knowledge acquired in classrooms and the practical skills required aboard ships. Key findings highlight the need for educational reforms, including increased practical training hours, curriculum adjustments to enhance relevance, and integration of simulation technologies. Participants, comprising professional engine masters, endurance managers, and maritime educators, unanimously advocate for a more hands-on approach to training that prepares cadets to handle complex engine maintenance tasks effectively. Simulation technologies, particularly virtual reality simulations, emerge as crucial tools for bridging the gap between theory and practice. They provide realistic environments for cadets to simulate diverse operational scenarios and develop critical decision-making skills without exposing them to actual risks. The study concludes with recommendations for collaborative efforts between educational institutions and industry stakeholders to implement these reforms, ensuring a competent and prepared workforce capable of meeting the dynamic challenges of modern maritime operations.

The purpose of this study is to investigate the impact of degradation on mangrove forest ecosystems and analyze innovative strategies used to maintain the sustainability of these ecosystems. The method used is the literature review method, in which various sources of relevant scientific literature are systematically evaluated. The results of this study show that mangrove forest degradation has serious consequences for coastal ecosystems, including biodiversity loss, increased risk of natural disasters, and decreased water quality. However, various strategies have been developed to address these challenges, including ecosystem restoration, sustainable management, local community participation, and the application of modern technology. A thorough discussion of the findings reveals that the integration of these strategies can provide effective solutions in preserving mangrove forests and strengthening the resilience of coastal ecosystems. The practical implication of this study is the importance of adopting innovative strategies in environmental management, while future research directions include increasing collaboration among stakeholders and developing more advanced technologies to support sustainable mangrove forest conservation.

This study investigates sustainable Islamic business models through a qualitative case study of green-tech startups in Central Java, Indonesia. Using semi-structured interviews with founders, managers, and employees, complemented by secondary data from business reports and sustainability disclosures, the research examines how Islamic ethical principles, including stewardship (khalifah), social welfare (maslahah), and justice (adl), are integrated into operational practices and sustainability strategies. The purposive selection of 3–5 startups enables an in-depth exploration of organizational decision-making, innovation adoption, and environmental and social performance. Thematic analysis identifies recurring patterns in ethical integration, technological innovation, and sustainability outcomes. Findings reveal that Islamic startups effectively embed ethical values into operations, fostering environmental accountability through renewable energy initiatives, waste reduction, and resource optimization, while enhancing community engagement and equitable service delivery. Technological innovations such as IoT, AI, and blockchain further support sustainability performance, enabling startups to monitor and optimize environmental outcomes without compromising financial viability. Comparative analysis demonstrates that Islamic startups outperform non-Islamic counterparts in key sustainability metrics, including carbon reduction and social impact, highlighting the competitive advantage of ethics-driven entrepreneurship. Overall, the study confirms that Islamic ethical frameworks foster both environmental accountability and technological innovation, providing a practical model for sustainable development in emerging economies. These findings offer valuable insights for policymakers, investors, and entrepreneurs seeking to align ethical, social, and environmental objectives with business strategy.

The integration of Green Internet of Things (Green IoT) systems in agriculture presents a promising solution for addressing critical challenges in water and energy usage. This study investigates the impact of real-time monitoring and data-driven irrigation control on resource optimization in precision agriculture. By incorporating soil moisture sensors, solar-powered IoT devices, and data analytics, the system aims to reduce water and energy consumption, enhancing sustainability in farming practices. The research finds that the Green IoT system can reduce water usage by up to 40% compared to traditional methods, while energy consumption is decreased by approximately 25% through the use of solar energy. The study also explores the advantages of implementing IoT-enabled systems, which ensure precise water delivery, preventing over-watering and under-watering, thereby improving crop yields and reducing waste. Despite these positive outcomes, the research identifies key challenges such as high initial costs, limited infrastructure in rural areas, and concerns related to data security. These barriers hinder the widespread adoption of Green IoT systems, especially in developing agricultural regions. Nonetheless, the findings highlight the potential of Green IoT to foster sustainable agricultural practices by promoting efficient resource use and reducing environmental impact. The study suggests that further research should explore the long-term economic implications of Green IoT adoption and investigate ways to overcome technical and financial challenges. Additionally, expanding the scope of Green IoT to other agricultural sectors, such as livestock farming, could enhance its applicability and overall impact on agricultural sustainability.

This study investigates the performance and sustainability of a hybrid solar-biomass drying system for agricultural products, focusing on its efficiency, environmental impact, and economic feasibility. The hybrid system combines solar energy and biomass combustion to create a continuous and reliable drying process. The key findings reveal that the hybrid system achieves over a 20% improvement in drying efficiency compared to solar-only and biomass-only dryers. This efficiency gain is attributed to the synergistic use of solar energy during the day and biomass energy during periods of low sunlight or at night, ensuring consistent drying conditions and reduced drying time. Additionally, the hybrid system significantly reduces CO₂ emissions, contributing to a more sustainable approach to agricultural processing. The environmental benefits of using renewable energy sources, as opposed to fossil fuels, align with the growing need for energy-efficient and eco-friendly agricultural technologies. Economic analysis suggests that the hybrid system is a cost-effective solution for small- to medium-scale farmers, particularly in rural areas where access to grid electricity is limited. The use of locally available biomass fuels further enhances the system’s sustainability and affordability. This study also discusses the practicality of implementing hybrid dryers in rural farming communities, emphasizing their potential to improve drying efficiency, reduce environmental impacts, and boost economic opportunities for farmers. Future research should focus on optimizing system integration, expanding biomass fuel options, and exploring automation to enhance the performance and scalability of hybrid drying systems.

In Indonesia, where different languages co-exist and where English is used as a foreign language, the learners' attitudes and motivational orientation toward English play an important role in formulating effective learning strategies. This article aimed to examine the attitudes of Indonesian students toward learning English as a foreign language and to understand the motivational orientations that result from their attitudes. A secondary objective of this research was to find the appropriate learning strategy that can be used for high school students in Indonesian education based on the result of the research. Fifty high school students in the second grade from sciences and social sciences major were asked to respond to questionnaires that measured their attitudes and orientations. The results indicated positive attitudes toward learning English as a foreign language with a clear instrumental orientation. Hence, the study highlighted that Self-Regulated Learning (SRL) became the appropriate learning strategy. Despite a strong desire to learn English to gain access to international markets, the status of Bahasa remains secure as it is viewed as a symbol of national integration.

This research explores the influence of Korean dramas on the communication styles of students from the Faculty of Da'wah, specifically the 2022 cohort. Drawing on communication theory and cultural studies, the study investigates the impact of Korean dramas on interpersonal communication, non-verbal cues, and language use among the mentioned group. Through surveys and interviews, the research identifies key communication patterns influenced by exposure to Korean dramas, shedding light on the integration of Korean cultural elements into the communication practices of these students. The findings contribute to understanding the role of popular media in shaping communication norms within diverse cultural contexts, emphasizing the need for media literacy and cultural sensitivity in contemporary education.

The concepts used in this research are persuasive communication, film and semiotics. This type of research is descriptive qualitative and uses Charles Sanders Peirce’s semiotic analysis method. The data collection techniques used are observation and documentation studies.The results of this research are that there is meaning in the persuasive communication carried out by Jeong Myeong Seok through the persuasive techniques used, namely dissonance/cognitive dissonance techniques, he influences followers to carry out attacks, association techniques, through the message that the mother willThis life and reality is true, the integration/empathy technique where he uses dialogue language to check the victim that he was assigned by God, the reward/payoff idea technique which he does with a promise, the Fear Arrousing technique, he uses threat techniques to attractthe hearts of the victims, the arrangement technique, this technique he uses to attract the attention and support of his followers and finally the red-hearing technique The conclusion of research on the documentary film In The Name Of God: A Holy Betrayal, the story of Jeong Myeong Seok through Charles Sanders Peirce’s approach from these three episodes, found that there was persuasive communication with the dialogue signs used by Jeong Myeong Seok and throughpersuasive communication contains misdirection in its teachings.