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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.

This community service programme was implemented in the coastal community of Padelegan Village, Pademawu Subdistrict, Pamekasan Regency, with a focus on strengthening health literacy and behaviour-based disease prevention at the household level. The background of the activity is based on the vulnerability of coastal communities to health problems related to hygiene, household drinking water management, and environmental cleanliness, as well as the need for an approach that not only increases knowledge but also encourages consistent preventive practices. The programme aims to improve residents' ability to understand and use health information in an applicable manner, while strengthening key disease prevention behaviours through education and mentoring. The activity will be carried out from February to April 2025 with a behaviour-based education design combined with practice demonstrations and community mentoring, involving 48 participants (40 residents/households and 8 posyandu/PKK cadres). The stages included initial assessment, development of KIE media (brief modules and leaflets/posters), two education-demonstration sessions, two home mentoring sessions, and final evaluation. The evaluation was conducted using pre-post knowledge, attitude, and practice (KAP) assessments and practice observation through a checklist. The results showed an increase in health literacy and improved consistency in preventive behaviour; knowledge increased from 5.4 to 7.8, and compliance with hand washing at critical times increased from 41.7% to 72.9%. Programme outputs included an information, education and communication (IEC) package, a household monitoring checklist, and capacity building for cadres as local facilitators. It was concluded that the integration of participatory education, practical demonstrations and cadre mentoring has the potential to be effective in encouraging preventive behavioural change in coastal communities, with recommendations for integrating monitoring into the routine agenda of integrated health service posts (posyandu) and community empowerment groups (PKK) and for follow-up monitoring for 3–6 months to maintain the sustainability of practices.

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.

Limited legal literacy among village officials may contribute to disorderly village administration, inconsistent document formats, weak record-keeping, and poor archive traceability, ultimately affecting public service quality. This Community Service Program (Pengabdian kepada Masyarakat/PKM) aimed to strengthen the legal literacy of village officials in Larangan Luar Village, Pamekasan Regency, to improve orderly and accountable village administrative governance. The program applied a hands-on training model combined with mentoring/document clinics and a pretest–posttest evaluation involving village officials (n = 18). Implementation consisted of three core training sessions and two mentoring sessions over four weeks, focusing on document legality, official correspondence standards, numbering and registers, and basic archive management. Results showed an increase in the average legal literacy score from 54.1 (pretest) to 74.6 (posttest), an improvement of 20.5 points. Beyond knowledge gains, document quality and administrative order improved through the adoption of practical administrative tools developed during the program. Key outputs included a concise module (±22 pages), 10 village administrative document templates, one village administrative SOP, and a simple filing system based on document classification and folder numbering. In conclusion, strengthening village officials’ legal literacy through practice-based training and mentoring effectively supports improvements in village administrative governance and is potentially replicable in other villages in Pamekasan with context-specific adjustments.

This research focuses on the design and evaluation of a novel parallel graph optimization algorithm incorporating dynamic load balancing (DLB) to address inefficiencies in heterogeneous computing environments. Large-scale graph optimization problems, such as those in social networks, bioinformatics, and transportation systems, often suffer from computational imbalances when using traditional static load balancing approaches, leading to underutilized resources and prolonged execution times. The primary objective of this research is to develop an algorithm that can dynamically adjust workload distribution across processors, enhancing computational efficiency and scalability. The proposed method combines heuristic techniques, including region expansion and multilevel partitioning, with diffusive load balancing strategies to minimize inter-processor communication overhead. Experimental results demonstrate that the proposed algorithm reduces execution time by up to 40% compared to static methods, with optimized resource utilization and more balanced workload distribution. The scalability of the algorithm is also evident, as it adapts effectively to increasing problem sizes and processor counts. These findings suggest that dynamic load balancing is crucial for improving parallel graph optimization in real-world applications. Future work will focus on further enhancing the algorithm’s responsiveness to rapidly changing workloads and expanding its applicability to additional domains.

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 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.

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 Community Service Program (PkM) focused on mentoring storytelling competitions was implemented as an effort to improve students' competence in the art of storytelling, while also supporting their preparation for the FLS3N event. The activity was designed through a series of training sessions that included understanding the concept of storytelling, examples of storytelling practices, hands-on practice, and periodic evaluations. During the mentoring process, students showed progress in terms of story mastery, intonation, expression, and confidence. The approach used encouraged active participation so that participants were able to improve their storytelling techniques based on the feedback provided. The results of this activity show that structured and continuous training can improve students' readiness to present stories in a more communicative and interesting manner in upcoming competitions.

Digital transformation (DT) has become a critical component for organizations aiming to enhance their operational efficiency, innovation, and competitiveness. However, many organizations struggle to achieve successful digital transformation due to the misalignment between their Enterprise Information Systems (EIS) and organizational strategic goals. This research seeks to design and validate a model for aligning EIS with digital transformation strategies to improve organizational effectiveness. By adopting the Design Science Research (DSR) approach, this study develops a practical model that integrates strategic planning, process management methodologies, and emerging technologies to facilitate alignment between IT and business strategies. The research includes key steps such as requirement analysis, artifact design, expert validation, and case study evaluation to ensure the model's robustness and applicability across different organizational contexts. Findings indicate that the proposed model significantly improves strategic-system alignment, enhances decision-making consistency, and facilitates better integration between business and IT units. The model also addresses common challenges such as resistance to change, skill gaps, and misalignment, fostering a supportive culture for digital transformation. In comparison to existing descriptive frameworks, the proposed model is more structured, adaptable, and actionable, providing organizations with a clear framework to guide their digital transformation efforts. This research contributes to the growing body of knowledge on EIS alignment and offers practical insights for organizations seeking to achieve successful digital transformation. Future research could explore the model's application in various organizational settings and examine its impact on long-term organizational growth and innovation.

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.  

This community service activity aimed to strengthen teachers’ competencies in utilizing digital platforms to support project-based learning at a vocational high school. The background of this activity was the limited ability of teachers to manage project-based learning in a structured and systematic manner using digital platforms. The method employed was participatory mentoring, involving needs analysis, activity planning, implementation of digital platform mentoring, monitoring and evaluation, and reflection with follow-up actions. The participants were teachers of SMK Negeri 1 Tukak Sadai, Bangka Barat. The results indicated an improvement in teachers’ understanding and skills in designing, managing, and evaluating project-based learning using digital platforms. Teachers became more confident in integrating technology into learning processes and demonstrated better organization of project activities. This activity contributed to enhancing teachers’ digital competencies and improving the quality of project-based learning practices in vocational education. The findings suggest that participatory mentoring is an effective approach to supporting sustainable digital learning innovation in vocational schools.

The increasing prevalence of metabolic syndrome and chronic kidney disease underscores the need for simple, low-cost, and community-applicable screening indicators. This study aimed to evaluate the association between basic anthropometric parameters and blood pressure, metabolic indicators, and renal function among adults in the Badui Luar community. A cross-sectional design was employed involving 41 participants who underwent anthropometric assessment, biochemical measurements, and blood pressure evaluation. Pearson correlation analysis was used to examine linear associations between variables. The results demonstrated that neck circumference exhibited significant correlations with dyslipidemia components, including LDL (r = 0.377), TC/HDL ratio (r = 0.516), and HDL (r = –0.433), indicating cervical adiposity as a strong marker of atherogenic risk. Calf circumference showed protective correlations with fasting glucose (r = –0.352) and eGFR (r = 0.322), suggesting the metabolic relevance of peripheral muscle mass in glycemic regulation and renal status. Body mass index showed a weak correlation with systolic blood pressure (r = 0.149), whereas waist and hip circumferences exhibited mild, clinically insignificant correlations with triglycerides and total cholesterol. Overall, these findings highlight that simple anthropometric measures—particularly neck and calf circumference—may serve as early indicators of cardiometabolic and renal risk in community-based screening. Further longitudinal studies with larger and more heterogeneous populations are required to validate these associations and determine the predictive power of anthropometric indicators.

Aortic dissection is a cardiovascular emergency characterized by a tear in the aortic intima layer and the formation of a false lumen, posing risks of fatal complications such as rupture and organ malperfusion. Stanford type B dissections may be managed conservatively; however, complicated cases require intervention. Thoracic Endovascular Aortic Repair (TEVAR) has emerged as the preferred modality, offering lower perioperative morbidity and mortality compared to open surgery. To report the first TEVAR procedure performed in Manado on a patient with Stanford type B aortic dissection, highlighting the technical approach, operative challenges, and clinical outcomes. A 62-year-old male with a history of hypertension and chronic chest pain was referred following a CT angiography that revealed a Stanford type B aortic dissection with an entry site in the proximal descending aorta. TEVAR was performed with stent-graft placement in zone 2 and a chimney graft to preserve perfusion of the left subclavian artery (LSA). Intraoperative aortography confirmed false lumen exclusion without endoleak. The patient showed significant clinical improvement and recovered without postoperative complications. The procedure was technically and clinically successful, with no major complications. Postoperative evaluation confirmed stable stent position and maintained LSA perfusion. This case demonstrates that TEVAR can be safely implemented in non-tertiary regional hospitals with thorough preparation. The first TEVAR procedure in Manado illustrates that this technique can be an effective solution for complicated type B aortic dissections. This experience marks a milestone in the development of endovascular services in resource-limited settings and may serve as a reference for similar institutions in Indonesia.

The need for learning materials that can adapt to students’ abilities and characteristics has made the development of AI-based adaptive learning resources increasingly important in elementary education. However, limited digital literacy and high workloads among teachers hinder optimal implementation. This Community Service Program aimed to enhance the competencies of teachers at Sekolah Alam Ananda Mandiri Slawi through workshops, guided practice, and hands-on development of AI-supported adaptive materials. The method included preparation, conceptual presentation, technology demonstration, guided practice, pre–post evaluation, and follow-up support. The results showed a significant improvement, with teachers’ competence in the “good” category increasing from 22.5% to 82.5%, and all participants (100%) successfully producing at least one adaptive module and one deep-learning-based material component. Furthermore, 92% of participants reported that the program greatly improved their digital literacy and pedagogical understanding. Thus, this activity effectively strengthened teachers’ competencies and provided a strong foundation for the sustainable integration of AI-based adaptive learning materials in elementary classrooms.

This study aims to describe the role of Biology Education Community Service Program students in improving the quality of biology learning at SMPN 1 Mappakasunggu. This study used a descriptive qualitative approach with data collection techniques in the form of observation, interviews, and documentation. The results showed that the Community Service Program students played an active role in various aspects, from lesson planning, implementation of teaching and learning activities, to evaluation of learning outcomes. In addition, the Community Service Program students were also involved in mentoring simple practicums that helped students understand biology concepts more practically. They also contributed to the development of innovative learning media that facilitated students' understanding of the material. The presence of Community Service Program students at the school had a significant positive impact on improving students' conceptual understanding, motivation, and learning activity. Students showed higher enthusiasm in participating in biology learning and experienced improvements in their learning outcomes. Thus, the Community Service Program teaching program is a tangible form of student contribution in supporting the improvement of education quality, especially biology learning in schools with limited resources. This program also provides valuable experience for students to be directly involved in the world of education and have a positive impact on society.

This community service program aims to strengthen the managerial capacity of red lemongrass farmers in AMKE Oro-Oro Ombo, Batu, East Java, through training on cash bookkeeping, income analysis, and farming feasibility evaluation. The main problems faced by farmers are limited knowledge of financial management, lack of systematic bookkeeping, and inability to evaluate the profitability of their businesses, which often results in inaccurate decision-making and low competitiveness. The method applied included participatory training, mentoring, and simulation of financial records and feasibility analysis. The results of the program demonstrate that participants gained a better understanding of the importance of structured financial recording. Farmers who previously relied only on sales revenue calculations without considering fixed and variable costs are now able to prepare cash books, identify expenses, and evaluate profit margins accurately. Cash bookkeeping has proven to improve financial transparency and accountability, while income analysis has equipped farmers with the skills to assess their business sustainability. The feasibility evaluation, using indicators such as R/C and B/C ratios, showed that red lemongrass farming is profitable but still requires efficiency improvements to increase long-term sustainability. Overall, this training has enhanced farmers’ managerial skills, encouraged independent decision-making, and strengthened their capacity to face price fluctuations and market competition. The program is expected to support the continuity and competitiveness of red lemongrass agribusiness in AMKE Oro-Oro Ombo

The rapid growth of information technology has encouraged small and medium-sized food businesses to adopt digital systems in order to improve service quality and operational efficiency. Ayam Goreng Pakdhe, a local culinary business, still relies on manual ordering processes that often lead to order inaccuracies, slow service, and limited access to sales data. This study aims to design and develop a web-based ordering system for Ayam Goreng Pakdhe using the Spiral software development method, which emphasizes iterative development, risk analysis, and continuous user feedback. The research method consists of requirement analysis, system design, implementation, and evaluation carried out in repeated cycles according to the Spiral model. Data were collected through observation, interviews with business owners and employees, and analysis of existing business processes. The results show that the proposed web-based system is able to streamline the ordering process, reduce human errors, and provide real-time information on orders and sales reports. Furthermore, the iterative nature of the Spiral method allows the system to be adjusted according to user needs and potential risks identified during development. The implementation of this system is expected to enhance operational efficiency, support better decision-making, and increase customer satisfaction, thereby contributing to the digital transformation of small-scale culinary enterprises.

The Muhammadiyah Orphanage in Pamekasan faces major challenges in developing life skills and digital education for its children due to limited facilities, teaching staff, and conventional learning methods. To address these issues, an edutainment-based approach and digital pedagogy intervention were implemented to enhance learning quality, motivation, and preparedness for future social and technological challenges. The activities included workshops and training on Digital Pedagogy and Edutainment learning materials, as well as simulations and role-plays using a Game-Based Learning approach, followed by evaluations and participant plan presentations. The program significantly improved the wards’ digital literacy, particularly in personal security (online safety), digital ethics (cyber ethics), gadget usage, and information management, with the average score rising from 2.84 to 4.10 on a 5-point scale, surpassing the target of 75% of participants in the “good” category. Beyond cognitive aspects, the program also boosted motivation, engagement, communication, problem-solving, and independence. Caregiver training was also provided to ensure program sustainability. It is recommended that the orphanage integrate the Game-Based Learning Digital Safety module into its non-formal curriculum, enhance caregiver capacity through advanced training, and improve IT infrastructure.