This research aims to compare the performance of two forensic investigation methods, the National Institute of Justice (NIJ) and the National Institute of Standards and Technology (NIST), specifically for evidence analysis of MicroSD cards. MicroSD cards are frequently used as external storage in various digital devices, making them critical in digital forensic investigations. The study evaluates the effectiveness of these methods using tools such as Access Data FTK Imager and autopsy. The NIJ method enthis comparative passes detailed stages of preparation, collection, examination, analysis, and reporting, while the NIST method includes stages of collection, examination, analysis, and reporting. Results indicate that the NIJ method provides more comprehensive and detailed results, while the NIST method offers a faster investigation process. Additionally, tables and graphs illustrating performance metrics are included to substantiate the findings. This comparative analysis provides valuable insights for technopreneurs in optimizing digital forensic methods for better data integrity and efficiency, ultimately enhancing decision-making processes in technological entrepreneurship. Furthermore, this study aligns with the United Nations' Sustainable Development Goals (SDGs), particularly Goal 9: Industry, Innovation, and Infrastructure, by promoting innovative forensic methods that support the development of resilient infrastructure and foster innovation in the digital age. This study highlights the importance of effective forensic methods in supporting technopreneurial ventures.

This study explores the application of data science to analyze and improve student learning outcomes within educational environments, responding to the increasing demand for data-driven approaches in education. The objective is to identify key performance indicators that influence learning success and to develop predictive models that support personalized academic interventions. The research applies a mixed-method approach, combining quantitative data analysis from student records and qualitative insights gathered from educational stakeholders. Machine learning algorithms and statistical models are employed to identify patterns and relationships within large datasets, helping to pinpoint factors such as attendance, engagement levels, and assessment performance that most strongly correlate with learning outcomes. Results indicate that predictive models can effectively forecast student performance, allowing educators to proactively support at risk students and tailor learning experiences to individual needs. Furthermore, the findings demonstrate that integrating data science tools into educational decision-making can improve not only academic outcomes but also institutional strategies for student success. This study concludes that data science offers substantial potential for enhancing learning environments, enabling a more responsive and personalized education system that supports each student’s unique journey towards academic achievement

Quantum-resistant cryptography develops cryptographic protocols and algorithms that can withstand attacks from quantum computers. Quantum computers can solve certain mathematical problems much faster than classical computers, making current encryption techniques like RSA and ECC vulnerable to quantum algorithms such as Shor's algorithm. To address this, quantum-resistant cryptography focuses on creating new algorithms based on mathematical problems that are difficult for quantum computers to solve efficiently. Examples include lattice-based, code-based, hash-based, and multivariate polynomial cryptography. The National Institute of Standards and Technology (NIST) is leading efforts to standardize these algorithms to ensure they are compatible with various systems and applications. While these quantum-resistant algorithms offer protection, they may involve higher computational complexity and larger key sizes compared to traditional methods, posing implementation challenges that ongoing research aims to address.

Background: The problem of how to deal with waste is a global issue all countries face. Like many developing countries, Indonesia has inadequate infrastructure to process the extremely high volume of waste produced throughout the country and minimal public participation in proper waste management. Although the Indonesian government regulates waste banks as a community-based waste management solution, there is a lack of integrated technological innovations to support waste banks. This study fills the gap by developing Circonomy, a model combining Internet of Things, gamification, and semantic web technologies to advance community-based circular waste management. Objective: The aim of this study is to develop Circonomy as a circular waste model that integrates an Internet of Things–based smart bin, semantic web, and gamification as an innovative technological solution. Methods: We identified the problem by observing Indonesian waste banks at 3 locations in Jakarta and Yogyakarta to define and design Circonomy. The Circonomy prototype was developed using the Design Science Research Methodology and evaluated based on technical performance and user experience. The technical performance has three indicators: bin capacity accuracy with a minimum of 80% precision, bin lid response time <5 seconds for a minimum of 80% of trials, and data transmission success rate of at least 80%. The user experience metric has two indicators: a minimum of 80% reporting high usability and ease of use, and at least 80% of users reporting that they feel more motivated using the prototype than the traditional waste bank. We selected 10 random participants aged 18-60 years to perform a user experience evaluation of our prototype. Results: The Circonomy prototype demonstrated sound and stable performances related to technical performance and user experience. Circonomy achieved at least 80% technical performance accuracy, comparable to industry standards. The accuracy problem lies in the placement of the ultrasonic sensor. The waste should be placed directly under the ultrasonic sensor to ensure the bin’s capacity measurement accuracy. The user experience testing results from 10 participants indicated that Circonomy has excellent user engagement, and 100% felt motivated by gamification and 80% found the mobile app easy to use. Conclusions: The testing results showed that Circonomy has acceptable performances for early-stage prototyping, with at least an 80% accuracy rate in technical performance and user experience. This ensures that Circonomy operates effectively in real-world conditions while remaining cost-efficient and scalable. For future development, Circonomy will prioritize enhancing the accuracy and reliability of sensor-based occupancy detection through improved sensor placement, the integration of multiple sensors, and an exploration of alternative technologies for regions with limited IT resources. In addition, more gamification features such as challenges and quizzes should be added to improve user experience and motivation.

Future trends in AI and green computing for security are crucial areas of research and development in the realm of information technology. The integration of artificial intelligence (AI) and machine learning (ML) in cloud computing is a significant trend that is enhancing data security and operational efficiency. AI is envisioned to play a pivotal role in empowering intelligent, adaptive, and autonomous security management in 5G and beyond networks, enabling faster and more accurate decisions. Additionally, AI is at the forefront of triggering digital security innovations to address emerging threats in the post-COVID-19 world. Moreover, the proactive use of AI in predicting and preventing cyberattacks before they occur is a promising approach to enhancing security measures. As AI continues to advance, it is expected to have a transformative influence on military power, strategic competition, and international security. The use of AI as force multipliers for defensive and offensive cyber weapons is anticipated to revolutionize cybersecurity.

This research explores the intersection of artificial intelligence (AI) and cybersecurity, with a focus on how AI technologies are revolutionizing threat detection and incident response. AI-driven solutions are increasingly integral to identifying security risks in real-time, while enhancing operational efficiency across cloud services and other sectors. Key advancements in machine learning, deep learning, and automation are analyzed for their contributions to cybersecurity resilience. Additionally, the study examines the challenges posed by AI's integration, including the risks of biases, ethical considerations, and the complexity of AI-driven systems. The discussion highlights future trends, such as the role of AI in predictive analytics and its potential to mitigate evolving cyber threats. The implications for cybersecurity frameworks and practices emphasize the need for adaptive and ethical approaches to harness AI's full potential.