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Innovation in the education sector has become increasingly important alongside the rapid advancement of digital technology, which has enabled the emergence of various platforms and applications designed to enhance teaching and learning quality. One area significantly impacted by this transformation is educational evaluation, where digital solutions offer more efficient, accurate, and data-driven assessment processes. This study aims to identify and analyze the role of the Evaloexam application in driving educational innovation while fostering the development of technological entrepreneurship among its users. The research employed a quantitative methodology using a survey approach involving educators and students at the senior high school (SMA) level who actively utilize the Evaloexam platform. A total of 100 respondents were selected through random sampling techniques, and data were collected over one month using structured online questionnaires. The collected data were analyzed using both descriptive and inferential statistical methods to measure user perceptions, system effectiveness, and innovation impact. The findings indicate that Evaloexam plays a significant role in facilitating educational evaluation processes by streamlining exam administration, automating grading, and providing analytical insights into student performance. These capabilities contribute to improved efficiency, reduced administrative workload, and more objective assessment practices. Furthermore, the application encourages the development of technological entrepreneurship by stimulating user interest in digital product innovation, educational technology development, and technology-based problem solving. The results are consistent with prior studies emphasizing the positive relationship between technology integration and improvements in educational effectiveness and operational efficiency. Practically, this research suggests that Evaloexam and similar digital evaluation platforms hold strong potential for broader implementation within educational systems to support innovation ecosystems and technology-driven learning environments. However, this study is limited by its relatively small sample size, focus on a single application, and short data collection timeframe. Future research is recommended to involve larger and more diverse populations, extended study durations, and comparative analyses across multiple evaluation platforms to obtain more comprehensive findings.

The integration of IT Governance and expert system design offers transformative benefits for enhancing library user services. This research employs the COBIT 5.0 framework to align IT strategies with library objectives while developing an expert system tailored for personalized recommendations. The findings indicate that the expert system significantly improves operational efficiency, service accuracy, and user satisfaction by using user profiles to recommend relevant materials and streamline the borrowing process. Testing revealed high user satisfaction levels, with 96.6% finding the system effective and 100% confirming its efficiency. Additionally, IT Governance ensures strategic integration between technological infrastructure and service quality objectives, enabling data-driven decision-making. The study also highlights challenges, such as the need for robust data management and user training, suggesting areas for future improvement. Recommendations include incorporating machine learning to enhance system intelligence, conducting regular evaluations to maintain system relevance, and testing the scalability of this approach across various types of libraries. By integrating IT Governance with an expert system, this research sets a strong foundation for modernizing library services to better meet user expectations in the digital era.

The increasing demand for sustainable and reliable energy solutions in remote and off-grid communities has led to the exploration of hybrid bio-solar renewable energy systems. This paper examines the implementation of a hybrid bio-solar system with energy storage to improve microgrid stability and support local sustainability. By combining biomass and solar power with energy storage, the system provides a reliable, clean, and cost-effective energy supply, addressing intermittency and enhancing grid resilience. The research covers system design, power flow simulation, voltage and frequency stability, and both economic and environmental analyses to assess system effectiveness. The results show that the hybrid bio-solar system significantly improves energy reliability, reduces carbon emissions, and offers a more stable energy supply compared to diesel or grid-based systems. The system's scale and configuration are crucial in optimizing cost-effectiveness, with larger systems yielding better long-term benefits. Local community involvement is key to managing and maintaining the system, ensuring its sustainability. Compared to other renewable energy hybrids like wind-solar or solar-diesel, the bio-solar system offers superior reliability and cost-efficiency, especially where biomass is abundant. The paper concludes with recommendations for scaling these systems and emphasizes the importance of supportive policies for accelerating sustainable energy transitions.

The increasing demand for efficient data management in higher education has highlighted the need for advanced information systems that support research and community service activities. At Universitas Jambi, the Sistem Informasi Penelitian dan Pengabdian kepada Masyarakat (SIMLPPM) is the primary platform for managing research data. However, its usability remains limited due to restricted accessibility, unstructured data categorization, and inadequate visualization tools. This study aims to enhance SIMLPPM by developing an interactive dashboard to improve data presentation, user trust, and decision-making efficiency. The research employs the Human-Organization-Technology FIT (HOT-FIT) model to evaluate user satisfaction and system effectiveness by surveying 128 faculty members. The findings indicate that leadership support, organizational structure, and facilities significantly influence user satisfaction and system adoption, while information and service quality have minimal impact. The study underscores the importance of system usability and administrative support over purely technical attributes. Future research should explore AI-driven analytics and expand the evaluation across multiple institutions to enhance data accessibility and user engagement in research management.

Dementia is a growing global health challenge due to the aging population and lifestyle changes. Early and accurate diagnosis is crucial but often difficult and costly. The Case-Based Reasoning (CBR) method in artificial intelligence offers a solution by mimicking human problem-solving based on past experiences. This study aims to develop and implement an efficient and reliable CBR-based dementia diagnosis system. The system is expected to analyze and compare patient symptoms and medical histories with documented cases to provide faster and more accurate diagnostic recommendations. The implementation of CBR in a web-based expert system using PHP and MySQL has proven effective, significantly contributing to the improvement of patient quality of life and healthcare system effectiveness.

Industrial IoT (IIoT) networks, critical for automation and smart manufacturing, are susceptible to faults due to their complexity and the large number of connected devices. This paper introduces a deep learning-based approach for early fault detection in IIoT networks. By leveraging recurrent neural networks (RNNs) and convolutional neural networks (CNNs), the system effectively identifies anomalies in real-time, helping to reduce system downtime and enhance operational efficiency in industrial settings.

Burglary involves forced or unauthorized entry, which leads to damage or loss of property having monetary or emotional value and, more severely, puts lives at risk. The dire need for the safety of lives and properties has attracted so much research on burglary alert system using Internet of Things (IoT) technology. Most of the research focused on alerting the users of burglary attempts using any or a combination of two notification methods: SMS, call, and email. This study emphasizes three-mode notification that combines SMS, call, and email using the application of IoT technology in a burglary alert system, which uses a Passive Infrared (PIR) sensor for burglar detection to ensure that Homeowners or authorized personnel get alerts in events of imminent attempt to break-ins. The study also details the sensor integration with its supporting components, such as the central hub or microcontroller, buzzer, LED, and network interface in the development of the system. The software was developed to facilitate seamless integration with the hardware, ensuring timely and accurate event detection and subsequent alert generation using Arduino IDE programming language, a framework based on the C++ language. The system effected the 3-mode notification to ensure that users get notification in case of an imminent break-in since the failure of the three modes simultaneously is extremely rare. The system’s performance based on its responsiveness on the 3-mode notifications was evaluated, and an average of 83.56% responsiveness was obtained, indicating an acceptable response time.