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The creative economy industry serves as a strategic pillar of the national economy, experiencing significant transformation in the digital era. This study aims to comprehensively analyze the pattern of human resource (HR) competency gaps within priority subsectors of Indonesia's creative economy and formulate effective, multi-stakeholder development strategies. Employing a Systematic Literature Review (SLR) methodology, this research rigorously analyzes 30 scientific journal articles, government reports, and publications from global institutions published between 2014 and 2024. The findings delineate three primary clusters of competency gaps: (1) The Digital-Technical Competency Gap, encompassing deficiencies in data analytics, specialized software mastery, and digital content creation tools; (2) The Digital-Business Competency Gap, which includes shortcomings in digital financial literacy, online business model development, and management of digital intellectual property rights; and (3) The Social-Cognitive Competency Gap, highlighting needs in adaptability, complex problem-solving, and effective virtual collaboration. In response, this paper proposes an integrative strategic framework grounded in a collaborative multi-stakeholder approach. Key recommendations include revitalizing educational curricula through industry-embedded learning and micro-credential integration, developing agile and accessible training ecosystems featuring bootcamps and digital platforms, and fostering supportive policies through fiscal incentives and the alignment of national qualification frameworks with digital skill standards. The successful implementation of this synergistic strategy is expected to significantly enhance the adaptability, innovation capacity, and global competitiveness of Indonesia's creative workforce, thereby ensuring the sustainable growth of the creative economy sector in the face of rapid digital disruption.

Graph theory as a branch of discrete mathematics has experienced significant development in its application to modern complex network systems, particularly in digital social networks and transportation systems. This research aims to analyze fundamental concepts of graph theory, examine characteristics of cycle detection algorithms along with their computational complexity, investigate their application in digital social network analysis, and explore their implementation in digital transportation system optimization. The research method employs a qualitative approach with library research focusing on scientific literature from 2020-2025 period from accredited academic databases such as Scopus, Web of Science, and IEEE Xplore, utilizing thematic analysis techniques to identify meaningful patterns from the examined literature. Research findings indicate that fundamental graph theory concepts including vertices, edges, and graph classifications form the foundation for relational structure modeling. Cycle detection algorithms such as Depth-First Search, Union-Find, and Tarjan demonstrate effectiveness with O(V+E) complexity for large-scale graphs. Applications in digital social networks facilitate community identification through Multi-View Clustering, centrality analysis for influencer detection, and understanding viral information dissemination patterns. Implementation in digital transportation systems demonstrates route planning optimization using Dijkstra and Bellman-Ford algorithms, vulnerability analysis through articulation point and bridge identification, and bottleneck detection with betweenness centrality. The research concludes that integration of graph theory in discrete mathematics education enhances critical thinking skills and real-world application understanding, with recommendations for algorithm development for massive dynamic graphs and machine learning integration in graph algorithm optimization.

This study aims to determine the effect of implementing the Problem-Based Learning (PBL) model on student learning outcomes in the Ignition System competency at SMKN 1 Katapang. The background of this study stems from the low understanding of students regarding the ignition system material due to the dominant use of the Teacher-Centered Learning (TCL) model, which tends to make students passive and only memorize concepts without understanding the overall working process. PBL is considered more relevant because it emphasizes real problem-solving, critical thinking, collaboration, and analysis according to constructivist theory. The method used is a quasi-experiment with a Nonequivalent Control Group Design. The research subjects consist of two classes of 11th-grade Light Vehicle Engineering students: an experimental group using the PBL model and a control group using TCL, with a total population of 70 students. Data collection was done through pretests and posttests using a validated multiple-choice objective test instrument. Data analysis includes comparing the learning outcome improvements of both groups to determine the effectiveness of PBL. The results show a more significant improvement in learning outcomes in the class using the PBL model compared to the TCL class. This proves that the implementation of PBL is effective in improving analysis skills and diagnostic skills in the ignition system. Therefore, PBL is recommended as a more suitable teaching model for practice-based subjects in vocational schools, especially in automotive electrical competencies.

In this study, the CIPP (Context, Input, Process, Product) evaluation model was used to assess the implementation of the People's School Education Program in its early stages. People's schools are an alternative education method designed to improve access to education for vulnerable socioeconomic groups. This study employed a descriptive qualitative methodology and employed a case study design. Data were collected through analysis of institutional documentation and in-depth semi-structured interviews with educators and administrators directly involved in program implementation. The four main components of the CIPP model were used to analyze the obtained data thematically. The results indicate that the program has been contextually designed to meet the needs of the target community. In terms of input, the program is supported by institutional commitment to implementation, although there are still limitations related to learning facilities and the availability of human resources. In terms of process, program activities are implemented adaptively, and in terms of product, initial progress is seen in increasing learning motivation and strengthening student character. Overall, the findings of this study demonstrate progress in the early stages of implementation and suggest that ongoing evaluation is needed to support program refinement and sustainability.

Curriculum management is a strategic component of Islamic education because it plays an important role in achieving educational objectives and internalizing Islamic values within the learning process. Nevertheless, in practice, many schools continue to face managerial challenges in implementing the curriculum effectively. This study aims to evaluate curriculum management at MTs Persis 23 Cirengit based on the core functions of Islamic Educational Management, namely Planning, Organizing, Actuating, and Controlling (POAC). The research employed a qualitative approach using a case study design. Data were collected through in-depth interviews, direct observation, and document analysis, and were analyzed using an interactive analysis model. The findings reveal that the POAC functions have been formally applied but have not yet operated as an integrated managerial cycle oriented toward continuous quality improvement. Curriculum planning remains largely procedural, organizing is not sufficiently participatory, implementation depends heavily on individual teacher capacity, and supervision is mostly administrative with limited follow-up actions.

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.

Concept drift, the phenomenon where the statistical properties of data streams change over time, poses a significant challenge in machine learning, particularly for long term data streams. Traditional machine learning models, including batch learning and non-adaptive approaches, struggle to detect and adapt to these changes, leading to degraded performance and inaccurate predictions. This study proposes an adaptive computational model designed to detect and respond to concept drift using incremental learning techniques and statistical drift detection mechanisms. The model integrates an Adaptive Drift Detector (ADD) and Incremental Learning System, enabling real-time adjustments to data distribution changes. The model is evaluated across synthetic and real-world datasets, demonstrating its superior ability to detect abrupt, gradual, and recurring drifts compared to traditional models. Experimental results indicate that the adaptive model maintains high prediction accuracy, minimizes false positive rates, and reduces detection delays. Furthermore, the model performs well in resource-constrained environments, making it suitable for real-time applications such as healthcare prediction, fault detection, and IoT systems. Despite its promising performance, the study identifies challenges related to computational complexity and the model’s performance with imbalanced datasets and noisy data. Future research should focus on optimizing the model’s scalability, computational efficiency, and adaptability to more complex data types to ensure broader applicability in dynamic environments. This work contributes to advancing the detection and adaptation of concept drift, offering a robust solution for dynamic and evolving data streams.

This study aims to determine the effectiveness of the Problem Based Learning (PBL) model integrated with Canva in improving students’ reflective thinking skills in the Aqidah Akhlaq subject at MTsN 3 Tuban. Reflective thinking is an essential competency that enables students to analyze moral behavior, evaluate decision-making processes, and connect Islamic ethical concepts with real-life experiences. However, preliminary observations indicated that students’ reflective thinking skills were still low and tended to remain at the level of theoretical understanding without deeper analysis. This research employed a pre-experimental design using a one-group pretest–posttest model. The subjects consisted of 30 eighth-grade students. The research instrument was a reflective thinking test developed based on indicators of moral evaluation, situation analysis, and experiential reflection, which had been validated through expert judgment. The learning process was conducted by applying the stages of Problem Based Learning integrated with Canva as a visual media to organize problem-solving steps and present students’ reflective outputs. Data were analyzed using descriptive statistics and a paired samples t-test. The findings showed a significant improvement in students’ reflective thinking skills after participating in PBL learning supported by Canva. Pretest scores ranged from 48 to 71 with an average of 59.67, while posttest scores increased to a range of 60 to 89 with an average of 71.20. The mean gain of 11.53 points was statistically significant as indicated by the t-test results (t = 10.39; sig. = 0.000), further supported by Cohen’s d value of 1.90, which falls into the category of a very large effect size. Qualitatively, students demonstrated enhanced abilities in identifying core problems, analyzing alternative actions, evaluating their cognitive processes, and visualizing moral reflections systematically through Canva. In conclusion, the PBL model integrated with Canva is effective in improving students’ reflective thinking skills in the Aqidah Akhlaq subject. This model not only enhances academic outcomes but also strengthens character development, creativity, and higher-order thinking skills, which are essential for 21st-century learning.

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.

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.

Electronics learning in higher education continues to face various challenges, particularly in the provision of interactive learning media capable of concretely and engagingly visualizing the form, characteristics, and working principles of electronic components. The limitations of conventional learning media often result in abstract learning processes, which in turn lead to a low level of student understanding of basic electronics concepts. This study aims to develop the AMPERE as an innovative and technologically relevant interactive learning medium. The research employed R&D approach using the Borg & Gall model, which includes the stages of needs analysis, design, product development, validity testing, and limited implementation. The AMPERE application was developed using marker based AR technology, in which a smartphone camera detects markers to display and interact with 3D electronic component objects in real time. The results indicate that the AMPERE application achieved a high level of validity, with a score of 0.88 from subject-matter experts and 0.84 from media experts, and was therefore deemed suitable for use as a learning medium. The small-group trial results showed a practicality level of 82.07%, while the practicality test during the implementation stage reached 85.67%. These findings demonstrate that AMPERE is effective in enhancing learning interactivity and assisting students in understanding the form, function, and working principles of electronic components through smartphone-based digital visualization. Theoretically, these results are consistent with constructivist learning theory, which emphasizes active knowledge construction through direct experience and interaction with learning objects.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

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.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

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 era of digitalization demands that the world of education adapt quickly to technological developments. One of the basic skills that elementary school students need to have is the ability to operate a computer, particularly in typing and using word processing applications. Community service research was conducted at the Kemala Bhayangkari Foundation 1 Medan with the aim of improving students' digital literacy thru typing training using Microsoft Word and the interactive media 10fastfingers.com. The training method is conducted using a workshop model, which consists of two main topics. The first material is a basic introduction to typing using the ASDF;LKJ pattern and the use of important keyboard keys. The second material is an introduction to basic Microsoft Word features, such as changing font type and size, using Bold, Italic, Underline, changing text color, and highlighting. Additionally, students are trained to use 10fastfingers.com to interactively improve their typing speed and accuracy. The research instruments are pretest-posttest, observation, and student response questionnaires. The research results show a significant improvement in typing skills. Average speed increased from 8–10 words per minute to 20–25 words per minute, with over 85% accuracy. Student responses were also very positive; they felt more confident, more motivated, and able to use Microsoft Word more effectively. This training program has proven effective in improving digital literacy among elementary school students while also fostering an interest in technology-based learning.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.