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The rapid development of digital technology highlights the importance of introducing computational thinking skills from an early age, including at the elementary school level. One effective approach to introducing basic programming concepts is through block-based coding learning media that are visual, interactive, and engaging. This community service activity aimed to improve elementary school students’ understanding and interest in basic coding through hands-on training using block-based coding media. The program was implemented with sixth-grade students at Yayasan Kemala Bhayangkari 1 Medan. The activity employed a hands-on training approach consisting of several stages, including an introduction to basic coding concepts, familiarization with the Blockly Games interface, and practical exercises involving puzzle and maze challenges designed to develop logical thinking, sequencing, and problem-solving skills. The evaluation was conducted through direct observation of student participation and assessment of students’ ability to complete the given challenges. The results demonstrated that the use of Blockly Games effectively increased students’ enthusiasm, active engagement, and understanding of basic programming logic. Students who initially perceived programming as difficult showed greater interest and confidence due to the colorful, visual block-based instructions that were easy to understand and enjoyable. This community service activity is expected to serve as an effective introductory model for coding education and to support the development of digital literacy among elementary school students.

Islamic character education has become a central focus of madrasah education; however, its planning is often implemented normatively without adequate managerial and evaluative mechanisms. This study aims to analyz ethe planning of Islamic character educationatMTs Persis 23 by examining the alignment betweent he madrasah’s strategic direction, curriculum, instructional practices, habituation programs, and character evaluation. Using a qualitative case study approach, data were collected through document analysis, classroom and school observations, and in-depth interviews with the head of the madrasah, the vice principal for curriculum, and home room teachers. Data were analyzed thematically to identify patterns of planning, implementation, and evaluation. The findings reveal that the madrasah has established a strong strategic foundation for character education through its vision, mission, and religious school culture, positioning character development as a core educational objective. Character values are integrated in to the Madrasah Operational Curriculum, lessonplans, and daily habituation activities functioning as a hidden curriculum. Nevertheless, the planning of character education remains weak at the operational level, particularly due to the absence of m easurable behavioral indicators, standar dizede valuation instruments, and systematic documentation of students’ character development. This study highlightsthe gap between normativ estrategic intentions and managerial implementation. It recommends the development of simple behavioral indicators, baselineand longitudinal character assessments, ands trengthenedpe dagogical supervision to ensure that character education planning is implemented systematically and sustainably.    

This community service activity aims to socialize the development of Human Resource (HR) competencies in the Fashion Design Department at SMK Al Ihsan to face the transformation of the fashion industry based on digital technology. The main challenge faced is the gap (mismatch) between the school curriculum, which still focuses on conventional production techniques, and the needs of an industry that is now digital and automated. The method used in this activity is a participatory and educational approach that includes four stages: needs observation, delivery of theoretical material (lecturing), focused group discussions (FGD) accompanied by application demonstrations, and evaluation. The results of the activity show an increase in participants' understanding of concepts such as eco-fashion, the creative economy, as well as the introduction of digital technologies like 3D design applications (CLO3D) and digital pattern making. Through the integration of four competency pillars hard skills, soft skills, digital skills, and entrepreneurial skills it is expected that graduate profiles can transform from operational seamstresses into competitive fashionpreneurs in the global market. The conclusion of this activity emphasizes that the Link and Match strategy and mastery of technological literacy are key to effectively reducing the skills gap of students in the Industry 4.0 era.

Usaha Mikro Kecil Menengah (UMKM) play a strategic role in Indonesia’s economy, serving as key drivers of inclusive economic growth and major contributors to employment creation. Despite their importance, many UMKM operators continue to face significant challenges in financial management, particularly due to the absence of structured and systematic financial record-keeping practices. This condition is also evident among UMKM in Kuning Gading Village, Unit XVIII Kuamang Kuning, where most businesses are still managed in a traditional manner with inadequate bookkeeping systems. This community service program aims to enhance financial literacy and strengthen the capacity of UMKM actors to implement simple bookkeeping as a foundation for effective business financial management. The methods employed include needs assessment through field observation, educational and practical training sessions, hands-on mentoring, and evaluation using pre-test and post-test instruments. The results indicate a significant improvement in participants’ understanding and skills related to transaction recording, separation of business and personal finances, and preparation of simple cash flow statements. Evaluation outcomes show an increase in competency scores from an initial range of 20%–40% to 75%–85% after the training. Furthermore, the program fostered a positive shift in participants’ mindset toward more professional and accountable business management practices. Therefore, the implementation of simple bookkeeping is proven to be an effective initial strategy for strengthening MSME financial governance and supporting sustainable business development at the local level.

The readiness of graduates to face the world of work is a crucial challenge amidst the increasingly competitive job market. This community service activity aims to improve student job readiness through job readiness training and strengthening soft skills. The methods used include needs analysis, CV preparation training, job interview simulations, strengthening soft skills, and pre- and post-training evaluations. The results of the activity show a significant increase in students' understanding of the world of work, self-confidence, and readiness to face the recruitment process. This activity is expected to become a model of sustainable service in increasing the competitiveness of graduates, by providing relevant and applicable provisions to face the challenges of the ever-evolving world of work. Strengthening soft skills is also expected to be a determining factor in students' success when entering the world of work. The sustainability of this program is expected to have a long-term positive impact on the development of graduates' competencies in various fields of work. In the future, the sustainability of this program is expected to have a long-term positive impact on graduates' competencies, strengthen their position in the job market, and support the development of skills needed in various fields of work that continue to evolve.

Papaya fruit (Carica papaya L.) is a tropical plant widely consumed as food and known to contain various bioactive metabolites with potential health benefits. The increasing interest in natural products as functional resources highlights the importance of reviewing the chemical characterization and health applications of papaya bioactive compounds. This article aims to systematically review the types of bioactive metabolites found in papaya fruit, the chemical characterization methods applied, and their potential applications in the health sector. This review was conducted through a literature study of relevant scientific articles, focusing on metabolite identification and biological activity evaluation. The results indicate that papaya fruit contains diverse bioactive metabolites, including flavonoids, phenolic compounds, alkaloids, saponins, terpenoids, and proteolytic enzymes. These compounds are commonly characterized using chromatographic and spectroscopic techniques. Several studies have reported that papaya bioactive metabolites exhibit biological activities such as antioxidant, antimicrobial, anti-inflammatory, and antidiabetic effects. This review suggests that papaya fruit has promising potential as a functional natural resource for health and pharmaceutical applications.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.

The increasing volume and complexity of data have made traditional 2D visualization methods insufficient for effectively exploring and understanding high-dimensional datasets. Immersive Virtual Reality (VR) presents a promising solution by providing an interactive 3D environment that enhances spatial understanding, task efficiency, and user satisfaction. This research aims to evaluate the user experience (UX) and interaction design quality of immersive VR interfaces for 3D data visualization in complex environments. The study employs a mixed-methods approach, combining usability testing, UX questionnaires, and task-based performance analysis. Participants interacted with VR prototypes designed to visualize complex data and were assessed on their ability to manipulate and explore the data efficiently. The findings show that immersive VR interfaces significantly improve spatial comprehension, reduce cognitive load, and increase task performance efficiency compared to traditional 2D systems. Additionally, user satisfaction was notably high, with participants appreciating the intuitive and engaging interaction methods. The study concludes that immersive VR can provide substantial benefits in real-world data visualization applications, particularly in domains requiring the exploration of complex and high-dimensional data. However, further research is needed to optimize VR interfaces and address challenges such as motion sickness and interaction complexity.

The integration of Augmented Reality (AR) technology into higher education has emerged as a promising approach to enhance collaborative learning experiences. This study aims to design and evaluate an AR multimedia framework that facilitates real time interaction and spatial visualization, creating immersive and engaging learning environments for students. The AR framework was developed with a focus on improving student engagement, collaboration, and learning outcomes through interactive 3D models and real time feedback. By leveraging AR technology, the study sought to address common challenges in traditional learning environments, such as limited student interaction and engagement, and lack of real time feedback. The experimental evaluation involved two student groups: one using the AR-based system and the other using conventional multimedia tools. Findings revealed that students using the AR framework showed significant improvements in engagement, interaction frequency, and collaborative task performance. Additionally, the AR framework contributed to better learning outcomes, including enhanced comprehension, retention of complex concepts, and improved problem-solving skills. The study also highlighted the importance of incorporating a user-centered design approach in developing AR applications to ensure that the system meets the needs and preferences of learners. Qualitative feedback from students indicated that the AR system provided an enriched learning experience, although challenges such as interface navigation were noted. Overall, the study demonstrates the effectiveness of AR in fostering collaborative learning and offers practical insights for its integration into higher education curricula. Future research should explore the integration of AR with other immersive technologies to further enhance collaborative learning experiences.

The increasing adoption of collaborative robots in modern manufacturing environments requires reliable perception systems that can ensure both safety and operational efficiency during human–robot collaboration. This study proposes a CNN-based real-time computer vision system for object and human detection in shared robotic workspaces. The research focuses on developing and evaluating a single-stage deep learning detection model optimized for real-time performance while maintaining high detection accuracy. The proposed methodology includes dataset preparation, model training using transfer learning, real-time system implementation, and comprehensive performance evaluation. Experimental results demonstrate that the developed system achieves high detection accuracy, as reflected by strong precision, recall, and mean Average Precision (mAP) values, while maintaining low inference latency suitable for real-time operation. The system consistently operates above real-time frame-rate thresholds, ensuring timely perception updates required for safety-related decision-making in collaborative robotic environments. Graphical and quantitative analyses further confirm the stability of inference performance under dynamic interaction scenarios involving human movement and multiple objects. Compared with existing approaches, the proposed system provides a balanced trade-off between accuracy and computational efficiency, making it practical for deployment in safety-aware human–robot collaboration scenarios. Overall, the findings indicate that CNN-based real-time object detection systems can effectively support perception and situational awareness in collaborative robotics, contributing to safer and more efficient industrial automation.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

User experience (UX) evaluation plays a crucial role in understanding how users interact with digital platforms and in improving product design. Traditional UX evaluation methods, such as surveys and interaction logs, often rely on a single data source, which limits the depth of analysis. This study explores the integration of multimodal data processing techniques in UX research, aiming to enhance the accuracy and comprehensiveness of UX evaluations. By combining interaction logs, visual attention data, and physiological measurements, this approach provides a more holistic understanding of user behavior, emotional responses, and satisfaction. Interaction logs offer objective data on user actions, while eye-tracking and physiological data capture users' emotional states, providing richer insights into usability and user experience. This study highlights the effectiveness of multimodal integration in identifying patterns that traditional methods overlook, such as emotional responses to interface elements and real-time feedback from users. The findings reveal that multimodal data processing improves the precision of UX assessment by combining objective behaviors with subjective emotional responses, offering a more complete view of user interactions. The study also discusses the challenges of data synchronization and the potential ethical concerns related to the use of physiological data. The integration of these data sources shows great potential for enhancing the design process, allowing designers to make informed decisions based on comprehensive insights. Finally, this research underscores the future potential of multimodal analytics in UX research, suggesting further exploration of additional data modalities and real-time applications in various digital environments.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

Indonesian language learning in elementary schools, especially on figurative language, still faces various problems, such as difficult to understand material, limited learning media, and low student interest and understanding. This is also experienced by fifth-grade students of SD 1 Mlati Lor who have difficulty recognizing types of figurative language and understanding their meaning well in context. This study aims to develop an interactive flipbook media about figurative language that highlights the local wisdom of North Sulawesi and test its feasibility and practicality for semantic learning. The method used in this study is research and development (R&D) with the ADDIE model, which consists of analysis, design, development, implementation, and evaluation. The research subjects were fifth-grade students of SD Negeri 1 Mlati Lor. Data collection was carried out through interviews, observations, validation by material and media experts, questionnaires for teachers and students, and learning outcome tests. Data were analyzed using qualitative and quantitative descriptive approaches. The results of the study indicate that the interactive flipbook media inspired by the local wisdom of North Sulawesi was considered very valid by experts. Furthermore, practicality testing demonstrated that teachers and students responded in the "very practical" category, indicating that the media was easy to use and well-received in learning. The application of local cultural elements has been shown to help students understand figurative language concepts more clearly and meaningfully. Therefore, the developed flipbook can be used as an alternative Indonesian language learning medium to improve students' semantic understanding and cultural knowledge in elementary schools.

Teaching fiction in Indonesian language classrooms is often constrained by text-centered instruction and limited use of engaging digital media. In the era of Generation Z learners who are visually and digitally oriented, innovative learning materials are needed to enhance motivation, interpretation, and comprehension of literary texts. This study aims to develop and evaluate a Scribus-based digital magazine for fiction learning by integrating the ADDIE model within a qualitative descriptive framework. The research adopted a qualitative descriptive design comprising five stages of the ADDIE model: Analysis, Design, Development, Implementation, and Evaluation. The study was conducted at SMP Negeri 10 Semarang, involving one Indonesian language teacher and fifteen Grade VIII students, who were selected through purposive sampling. Data were collected through observation, interviews, and documentation, and analyzed using the Miles and Huberman model (data reduction, data display, conclusion drawing). Media and content experts conducted validation to assess the feasibility, usability, and pedagogical quality of the content. Findings revealed that the Scribus-based learning media effectively increased students’ engagement and comprehension in analyzing fictional elements such as plot, character, and setting. Expert validation results indicated a high feasibility level, with mean scores above 85% in design and content quality. Teachers reported improved classroom interaction and creativity, while students expressed enthusiasm for the visual and interactive format. The study concludes that open-source tools, such as Scribus, can provide cost-effective and pedagogically sound alternatives for developing literary learning media. The integration of ADDIE and multimodal design promotes contextual, engaging, and sustainable learning experiences. Future research should explore multimedia enhancements, such as audio storytelling and cross-curricular applications, to broaden the pedagogical impact.

The rapid development of digital technology requires Micro, Small, and Medium Enterprises (MSMEs) to adapt by utilizing digital marketing and improving financial management practices. However, many service-based MSMEs have not yet optimized digital promotion and integrated bookkeeping in their business operations. This community service activity aims to enhance the promotion of service MSMEs through the implementation of digital marketing and to strengthen cost control through simple bookkeeping at AND CUSTOM LED. The methods used include an initial survey, practical mentoring, and evaluation of the activities. The mentoring process involved training in promotional content creation, the use of social media as a business communication tool, and the application of daily transaction recording. The results indicate an improvement in the business owner’s understanding and behavior regarding promotional management and financial recording. The MSME owner became more active and consistent in implementing digital promotion strategies and more disciplined in recording income and expenses. In addition, the mentoring encouraged a more independent and well-planned work attitude. Overall, this activity demonstrates that integrating digital marketing with simple bookkeeping can improve the quality of service MSME management and support business sustainability in the digital era.

This study compares the scalability and maintainability of three prominent programming paradigms-functional programming (FP), object-oriented programming (OOP), and declarative programming (DP)-in the context of distributed data processing systems. The research aims to evaluate how each paradigm performs under increased data volume and its ability to handle complex operations, while also assessing the ease of maintenance through code readability, modularity, and the flexibility of updating and debugging. The study employs a comparative experimental design, implementing identical data processing tasks, such as data aggregation, filtering, and transformation, across each paradigm. Key findings indicate that FP and DP outperform OOP in terms of scalability, with their stateless nature and high-level abstractions enabling efficient parallel processing and task distribution. FP, with its emphasis on immutability and concurrency, and DP, with its focus on describing desired outcomes rather than implementation specifics, both demonstrate superior performance in handling large datasets. However, while OOP excels in modularity and flexibility, its reliance on mutable state and shared resources hampers its scalability in distributed environments. In terms of maintainability, both FP and DP offer clearer, more maintainable code due to their abstraction levels, making them easier to update and extend. OOP, while modular, presents challenges in managing mutable state, complicating maintenance. This paper concludes with practical recommendations for developers on when to use each paradigm based on system requirements and suggests areas for future research, such as hybrid paradigms and long-term maintainability studies in real-world applications.

The analysis methods used in this study include a case study on the use of closed-loop recycling and an evaluation of biopolymer performance across various industries, both of which are important components in the transformation of the manufacturing industry toward a circular economy. The research findings indicate that recycled materials can reduce carbon emissions by thirty to fifty percent and save production costs by fifteen to twenty-five percent. Artificial intelligence-based sorting technology improves sorting efficiency to 95 percent, and closed-loop recycling maintains the mechanical properties of materials up to 90 percent after four cycles. The degradation rate of biopolymers like PLA and PHA reaches 60-80% within six months, although production costs are still 2-3 times higher. The integrated approach increases resource efficiency by 45% and reduces waste by 60%. To achieve successful implementation, Extended Producer Responsibility (EPR) policies, strategic infrastructure investments, and collaboration from various parties thru the triple helix model must work together.