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Menstrual stress is a common reproductive health problem among adolescent girls, which can interfere with learning activities, concentration, and overall quality of life. This community service activity aimed to improve the ability of female students to manage menstrual stress through education and practice of deep breathing techniques at SMPN 2 Rambutan, Banyuasin Regency. The activity used a one-group pretest–posttest pre-experimental design involving 30 female students who had experienced menstruation. Health education was delivered through interactive lectures, discussions, and practical demonstrations of deep breathing techniques, while evaluation was conducted using pre-test and post-test questionnaires on menstrual stress levels. The results showed a reduction in menstrual stress, with most participants shifting from moderate stress before education to mild stress after the intervention. These findings indicate that deep breathing techniques are effective in helping students independently manage menstrual stress. Therefore, health education combined with practical deep breathing exercises is recommended to be implemented continuously in schools as a promotive and preventive strategy for adolescent reproductive health.

Menstrual stress is a common reproductive health problem among adolescent girls, which can interfere with learning activities, concentration, and overall quality of life. This community service activity aimed to improve the ability of female students to manage menstrual stress through education and practice of deep breathing techniques at SMPN 2 Rambutan, Banyuasin Regency. The activity used a one-group pretest–posttest pre-experimental design involving 30 female students who had experienced menstruation. Health education was delivered through interactive lectures, discussions, and practical demonstrations of deep breathing techniques, while evaluation was conducted using pre-test and post-test questionnaires on menstrual stress levels. The results showed a reduction in menstrual stress, with most participants shifting from moderate stress before education to mild stress after the intervention. These findings indicate that deep breathing techniques are effective in helping students independently manage menstrual stress. Therefore, health education combined with practical deep breathing exercises is recommended to be implemented continuously in schools as a promotive and preventive strategy for adolescent reproductive health.

This study examined the operational performance of CV Fortis Sportwear Indonesia (FSI) before implementing the Balanced Scorecard approach, identified the factors that hindered each Balanced Scorecard perspective, and analyzed the company's operational performance when measured using this approach.This study employed a descriptive quantitative method, aimed at providing a systematic, factual, and accurate overview of CV FSI's operational performance based on the Balanced Scorecard approach. Data were collected through interviews, questionnaires, observations, and literature review to obtain information related to the four main perspectives of the Balanced Scorecard: financial, customer, internal business processes, and learning and growth.After implementing the Balanced Scorecard, the performance evaluation of CV Fortis Sportswear Indonesia became more comprehensive compared to the previous system, which focused solely on financial aspects. The financial perspective showed improvement, but the customer aspect remained adequate, with declining retention and increasing complaints. Internal business processes still faced inefficiencies and quality issues, despite the introduction of new technology. The learning and growth perspective also improved through training, but employee satisfaction remained moderate. The Balanced Scorecard proved to provide a more comprehensive performance assessment and identified the need for improvements in customer, internal processes, and human resource development.

Islamic education in the digital era faces significant challenges in improving the quality of learning and developing students' critical thinking skills. One promising solution is the implementation of deep learning-based Islamic education, which can facilitate the teaching of religious material more effectively and easily accessible via the internet. Deep learning has the potential to strengthen students' critical thinking skills and understanding of Islamic values ​​in a more interactive and personalized manner. This study used a literature review method to analyze various relevant literature sources. The results indicate that deep learning can increase student engagement, enable personalized learning, and provide real-time evaluation of learning progress. However, the implementation of this technology also faces challenges, particularly related to the availability and quality of relevant data. Therefore, efforts are needed to improve data quality and develop learning models tailored to student needs to achieve optimal results in technology-based Islamic education.

This community service program is entitled Improving Microcontroller Competence of Students of SMK Negeri 3 Pematangsiantar through Arduino Training Based on Industrial Practice and is implemented as a follow-up to the school's official request to invite university practitioners as guest teachers in order to strengthen industry-based vocational learning. The activity was carried out on November 27, 2025 at the Computer Laboratory of SMK Negeri 3 Pematangsiantar involving 50 grade XI students, and guided by Andi Setiadi Manalu, S.Kom., M.Ti as the main instructor of Arduino material which focuses on the introduction of microcontrollers, basic programming, sensor integration, and simple automation project design. The purpose of this activity is to improve students' technical competence in the field of embedded systems while fostering work readiness through real-world practice-based learning experiences. The implementation method uses an experiential learning and project-based learning approach, which combines brief conceptual explanations with direct practice, technical discussions, and problem-based project assignment completion. The evaluation results show an increase in students' understanding of microcontroller functions, programming logic structures, and the ability to connect hardware and software in one work system. In addition, students demonstrated increased motivation, confidence, and interest in industrial technology, as reflected in their active participation during the practicum and their successful independent completion of functional prototypes. Overall, this activity proved effective in strengthening the technical competencies of vocational high school students and supporting the implementation of the link and match policy between vocational education and the needs of the industrial world, while also emphasizing the strategic role of higher education institutions in supporting the improvement of human resource quality at the vocational high school level.

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.

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.

This study aims to analyze teachers' teaching strategies in improving the quality of science learning for upper grade students (IV, V, and VI) at the SD Inpres Perumnas II Makassar. The results show that teachers implement various strategies including selecting approaches that suit students' needs, using a variety of learning methods, actively involving students in learning activities, utilizing media that support conceptual understanding, and implementing continuous evaluation to assess learning success. All of these strategies complement each other and help create more engaging, understandable, and relevant science learning for students. Research findings also revealed that the choice of teaching strategy is influenced by several important factors: student characteristics, the characteristics of the science material being taught, the availability of learning media and facilities, the learning environment, and the desired learning objectives. These five factors serve as the basis for teachers to determine the most appropriate strategy, ensuring a more effective, focused learning process that enhances students' understanding of the material. Furthermore, this study identified several challenges teachers face in implementing science teaching strategies, such as diverse student abilities, time constraints, a lack of supporting media and facilities, sub-conducive classroom conditions, and uneven student motivation. These challenges require teachers to be creative and adapt strategies to ensure learning objectives are achieved and the quality of science instruction continues to improve.

This article aims to describe the teaching methods and practices of the Philosophy of Islamic Education course at STAI Darussalam. This research uses a qualitative descriptive approach with a field study method that focuses on the learning process, the competencies to be achieved, teaching methods, assignment formats, evaluation systems, and obstacles encountered during the course. Data were obtained through observation and information gathering related to the implementation of the Philosophy of Islamic Education course. The results show that the teaching of the Philosophy of Islamic Education at STAI Darussalam focuses on understanding the nature of Islamic education, developing students' critical thinking patterns, improving educational literacy, and linking philosophical studies to social and political realities. The lecture method remains the dominant method used by lecturers, with an emphasis on students' ability to define and explain basic philosophical concepts accurately as an indicator of understanding. Learning success is measured by changes in students' thinking, which becomes increasingly critical, reflective, and contextual in responding to educational issues and social justice issues.

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.

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.

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 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.

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 increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.

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.

This study was based on the low ability of third-grade students at SD Negeri 2 Wonorejo to understand syntactic material, particularly the use of conjunctions, due to a lack of interactive and contextual learning media. This study aimed to develop EKUJARSI E-Flipbook learning media based on Kudus local wisdom that has been tested for feasibility and practicality to improve student understanding. The research method used was Research and Development (R&D) with the ADDIE (Analyze, Design, Development, Implementation, Evaluation) development model. The test subjects included subject matter experts, media experts, classroom teachers, and third-grade students. Data collection instruments used validation sheets and user response questionnaires. The results showed that the EKUJARSI media was highly valid for use, as evidenced by a validation percentage of 89.6% from media experts and 86.15% from subject matter experts. The practicality test also showed positive responses, with teachers giving a score of 80% and students giving a score of 85.9%, which is considered very feasible. Specific findings show that the ease of use aspect received a perfect score of 100% from students, indicating that this media is very user-friendly. It is concluded that the EKUJARSI E-Flipbook is feasible and practical to be implemented as an innovative learning media that effectively integrates local cuisine to facilitate understanding of language concepts.

This community service activity aims to enhance students’ literacy through the application of ecoprint as an environmentally based creative learning medium at MAT Daarut Tahfidz Al-Ikhlas Banda Aceh. This program was motivated by the limited utilization of practice-based art activities as a means of strengthening students’ literacy. The implementation method employed a participatory-educative approach consisting of material socialization, ecoprint technique demonstrations, group-based hands-on practice, reflective discussions, and activity evaluation. The results indicate that students were able to understand the concepts and stages of ecoprint, produce works with various natural motifs, and demonstrate improvements in visual literacy and creativity. In addition, the activity increased students’ awareness of the use of natural materials and the importance of environmental friendly practices. Group-based activities also encouraged the development of social literacy through collaboration and communication among participants. Therefore, ecoprint can be considered an effective and contextual alternative learning medium to support the enhancement of students’ literacy in the school environment and has the potential to be sustainably developed.

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.

This study aims to develop an effective predictive model for identifying students at risk of academic dropout using the Decision Tree and Linear Regression algorithms. The data used are sourced from the public Kaggle dataset Students Dropout and Academic Success, which includes demographic, socioeconomic, and academic performance variables for each semester. The research method includes data preprocessing stages, such as data cleaning, label encoding for categorical variables, numeric feature normalization, and target class adjustment to focus on binary classification, namely Dropout and Graduate. The modeling process is carried out by comparing the performance of the two algorithms using evaluation metrics of accuracy, precision, and recall. The results show that the Decision Tree algorithm has superior performance compared to Linear Regression in mapping non-linear patterns in student data. Feature importance analysis revealed that the number of curricular units in the second semester and tuition payment status are the main predictors of dropout risk. These findings are expected to assist educational institutions in implementing early interventions to improve student academic success.