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This study aims to analyze the management of young preacher (dai cilik) training in developing students’ preaching skills at SD IT Al-Washliyah Kelambir Lima Medan using a management function approach (*planning, organizing, actuating, and controlling*). This study employed a descriptive qualitative method, with data collected through in-depth interviews, participant observation, and documentation. The research informants consisted of the principal and teachers responsible for the young preacher training program. The collected data were analyzed systematically to describe the implementation of management functions in the young preacher training activities and their contribution to the development of students’ preaching skills. The results showed that planning was carried out through the development of a practice-based preaching curriculum, organizing was implemented through task allocation and regular scheduling, implementation focused on *public speaking* practice, sermon material preparation, and moral development, while evaluation was conducted periodically through student performance assessments. The implementation of this systematic training management contributed to the improvement of students’ preaching skills in cognitive, affective, and psychomotor aspects. These findings indicate that management functions applied in a structured manner play a strategic role in supporting the development of students’ preaching competencies at the elementary school level. Furthermore, continuous training provides opportunities for students to enhance their self-confidence, communication skills, and readiness to effectively deliver Islamic preaching messages

This research investigates the integration of Sound Governance principles within the sustainable tourism policy cycle as a mechanism to mitigate institutional fragmentation and evaluation inefficiencies in developing economies. Adopting a sequential explanatory mixed-methods design, the study establishes a multidimensional evaluation framework congruent with the Sustainable Development Goals (SDGs). Policy performance is rigorously analyzed across five strategic domains: Economy, Social-Welfare, Culture-Education, Environment, and Governance. The quantitative methodology employs 17 adapted SDG indicators to assess policy efficacy, while the subsequent qualitative phase utilizes semi-structured interviews and stakeholder mapping to deconstruct complex power dynamics within multi-actor co-management structures. The empirical findings demonstrate that Sound Governance—predicated on transparency and accountability—acts as a fundamental catalyst for policy effectiveness. It significantly enhances destination sustainability through a structured input-process-output-outcome-impact pathway. Evidence from the case study of Penglipuran Village, Bali, validates these results, showing that while indigenous institutional legitimacy bolsters social responsiveness, it remains susceptible to economic dependencies driven by overtourism. Ultimately, this study asserts that embedding SDGs into the policy evaluation cycle elevates assessments from perfunctory administrative exercises to strategic instruments essential for ecosystem preservation and long-term demand stability. These insights establish Sound Governance as a strategic intangible asset, providing significant theoretical contributions to development administration and offering pragmatic frameworks for policy-makers managing sustainable destinations in competitive global markets.

Agentic artificial intelligence (AI) systems capable of autonomous goal-directed behavior, multi-step planning, tool use, multi-agent coordination, and iterative self-correction represent a transition from passive clinical AI tools toward systems that can participate in complex healthcare workflows. However, empirical evidence remains fragmented across clinical decision support, patient monitoring, and administrative applications, and no systematic synthesis has evaluated which agentic principles have been technically demonstrated and which have accumulated sufficient evidence to support responsible clinical deployment. We conducted a PRISMA-informed systematic review of peer-reviewed empirical studies published between January 2025 and April 2026. Searches across five bibliographic databases and Google Scholar, supplemented by citation tracking, identified 443 unique records for screening, of which 25 met the predefined PICOS and quality appraisal criteria. Evidence was synthesized using an evidence-informed seven-principle framework derived from the integration of agentic AI, clinical AI, and healthcare governance literature. This framework provides a structured lens for examining how agentic principles are evaluated individually and in combination, enabling a deployment-readiness perspective that extends beyond capability-focused assessments alone. The evidence base was concentrated on technical capability principles, whereas human oversight, safety, compliance, and equity-related evaluation received comparatively limited attention. Most studies remained at the laboratory, benchmark, or proof-of-concept stage, and none reported demographic-stratified performance outcomes. Overall, the findings suggest a structural asymmetry in agentic healthcare AI: empirical research is advancing agentic capabilities more rapidly than it is generating evidence for the oversight, safety, equity, and governance mechanisms required for responsible clinical translation.

Abstract: Detecting chili leaf diseases remains challenging due to the non-uniform manifestation of symptoms, local discoloration, small lesion regions, and visual similarity between disease patterns and natural leaf background variations. Although YOLO-based detectors provide favorable computational efficiency, lightweight variants often struggle to distinguish subtle lesion characteristics, while conventional attention mechanisms such as CBAM primarily rely on global feature aggregation and may overlook regional activation variability. To address these limitations, this study proposes a YOLOv9s-based detection framework integrated with a Region-Dispersion Channel Spatial Attention (RDCSA) module. The proposed module incorporates regional dispersion statistics, namely mean, standard deviation, and range, as channel descriptors to capture inter-region feature variability before applying spatial attention refinement. Experiments were conducted on the COLD dataset containing 532 original images from five chili leaf condition categories using a split-before-augmentation protocol to ensure objective evaluation. RDCSA was integrated at the P5 feature level and evaluated through attention placement analysis, component-wise ablation, sensitivity analysis, stability assessment, and comparison with modern attention mechanisms. The proposed YOLOv9s + RDCSA model achieved an mAP@50 of 0.894, mAP@50–95 of 0.773, precision of 0.858, recall of 0.861, and an F1-score of 0.859 with only a marginal increase in model parameters. The results suggest that regional dispersion-based attention improves feature discrimination while preserving computational efficiency, particularly for disease symptoms characterized by heterogeneous spatial patterns. Nevertheless, performance remains influenced by visually ambiguous symptom categories, indicating that further validation across multiple datasets and field conditions is required. Overall, the proposed RDCSA module enhances detection capability without substantially increasing computational overhead, making it a promising attention mechanism for lightweight plant disease detection systems.

This study was motivated by the lack of standardized and objective assessment instruments for the teaching of the Syofyani Minang Payung Dance at the junior high school level, resulting in an assessment process that remains largely subjective and fails to measure psychomotor, affective, and cultural aspects in a balanced manner. This study aims to design and test the validity and reliability of a performance assessment rubric for the Syofyani Minang Payung Dance in cultural arts education at SMP Negeri 1 Bukittinggi. The research method used is a mixed-methods approach with a sequential explanatory design. The research subjects consisted of three dance instructors serving as expert judges, one cultural arts teacher, and 33 junior high school students. The research instrument was a performance-based assessment rubric covering five competency indicators: basic movement techniques; alignment with musical rhythm and dynamics; expression and character interpretation; mastery of payung props and movement safety; and accuracy of floor patterns and group synchronization. Quantitative data analysis was conducted using IBM SPSS Statistics through the Corrected Item-Total Correlation validity test and Cronbach’s Alpha reliability test, while qualitative data was analyzed using descriptive-interpretive methods. The research results show that all indicators have validity scores above 0.30 and are therefore considered valid, and the Cronbach’s Alpha reliability score is above 0.70, indicating good internal consistency of the instrument. Furthermore, the interview results indicate that the rubric is considered relevant, clear, and aligned with the learning characteristics of Syofyani’s Minang Payung Dance at the junior high school level. Consequently, the developed assessment rubric is deemed suitable for use as an objective, standardized, and contextually appropriate assessment instrument for dance education rooted in local culture.

This study examines the integration of Sound Governance principles within the sustainable tourism policy cycle to address institutional fragmentation and evaluation inefficiencies in developing economies. Using a sequential explanatory mixed-methods design, the research develops a multidimensional evaluation framework aligned with the Sustainable Development Goals (SDGs), analyzing policy performance across five strategic domains: Economy, Social-Welfare, Culture-Education, Environment, and Governance. The quantitative phase utilizes 17 adapted SDG indicators to measure policy efficacy, while the qualitative phase employs semi-structured interviews and stakeholder mapping to deconstruct power dynamics in multi-actor co-management structures. The findings reveal that Sound Governance—specifically transparency and accountability—serves as a critical catalyst for policy effectiveness, significantly influencing destination sustainability through an input-process-output-outcome-impact pathway. Empirical evidence from the case of Penglipuran Village, Bali, corroborates these results, demonstrating that indigenous institutional legitimacy enhances social responsiveness but remains vulnerable to overtourism-driven economic dependency. Notably, the study demonstrates that integrating SDGs into the policy evaluation cycle transforms assessments from mere administrative formalities into strategic instruments for long-term demand stability and ecosystem preservation. These findings position Sound Governance as a strategic intangible asset, offering theoretical contributions to development administration and practical guidance for policy-makers navigating the complexities of sustainable destination management in competitive global markets

This study addresses the issue of suboptimal student academic performance, which is frequently linked to the selection of instructional strategies that do not actively engage learners. Learning outcomes represent a multidimensional construct involving knowledge acquisition, attitude development, and skill formation. Therefore, the application of an appropriate learning model is essential to support meaningful learning experiences. This research aims to investigate the impact of implementing a Problem Based Learning (PBL) model supported by audio-visual media on students’ academic achievement. A quantitative approach with a quasi-experimental design was employed. The study involved two groups: an experimental class receiving the PBL intervention and a control class experiencing conventional instruction. Data were collected through pretest and posttest assessments and analyzed using both descriptive and inferential statistical techniques. The findings reveal a statistically significant difference between the two groups, indicated by a significance value below 0.05. This confirms that integrating problem-based strategies with audio-visual support contributes positively to improving students’ learning outcomes. The results highlight the importance of student-centered learning environments in fostering deeper understanding and engagement.

This study aims to analyze the strategic management evaluation of the Sundanese Language Subject Teacher Forum (MGMP) in improving teacher performance at public senior high schools in Tasikmalaya City. The study focuses on leadership management, member participation, institutional support, and program relevance to teachers’ professional development needs. This research employed a qualitative descriptive approach. Data were collected through observations, in-depth interviews, and documentation involving MGMP administrators, member teachers, school principals, and supervisors. Data analysis used the Miles and Huberman interactive model, including data reduction, data presentation, and conclusion drawing. The findings revealed that the MGMP management has generally functioned effectively as a professional learning community. Participatory leadership patterns encouraged collaboration among teachers in designing learning tools and solving classroom problems. However, several challenges were identified, including inconsistent program evaluation, limited technological integration, unequal member participation, and insufficient funding support. The study also found that MGMP programs significantly contributed to improving teachers’ pedagogical competence, especially in lesson planning and instructional implementation. Nevertheless, assessment and evaluation competencies still require further strengthening. This study concludes that strategic management, sustainable evaluation, and institutional support are essential in optimizing MGMP functions to improve teacher performance and the quality of Sundanese language learning.

This study aims to evaluate the application of environmental management accounting in the management of work programs at the Bangkalan Regency Environmental Agency. The research is motivated by increasing environmental problems, particularly waste volume and limited management facilities, while environmental management accounting has the potential to assist agencies in identifying, measuring, and reporting environmental costs as a basis for decision-making and public accountability. This study used a qualitative method with a case study approach at the Bangkalan Regency Environmental Agency. Data were collected through interviews, observations, and documentation studies. The results show that the Environmental Agency has implemented environmental management accounting, but its recording is still manual and does not separate environmental costs from general operational costs. This condition complicates cost evaluation and program impact assessment. Work program management has been structured and evaluated regularly, but performance measurement still focuses on output, not long-term environmental impact. Information transparency is also still limited to activity publications. Overall, the application of environmental accounting is at a basic stage and has not been fully integrated into the work program planning and evaluation process. The findings of this study provide theoretical and practical implications in the form of strengthening understanding of the role of environmental management accounting and the need to improve the work program recording and evaluation system at the Bangkalan Regency Environmental Agency.

Integrity is a fundamental value in realizing clean, transparent, and accountable public sector governance. Various integrity assessment instruments have been implemented in the public sector in Indonesia, such as the Integrity Assessment Survey (SPI) by the Corruption Eradication Commission (KPK), the Integrity Zone (ZI) by the Ministry of Administrative and Bureaucratic Reform (PANRB), and IntoSAINT developed by INTOSAI. However, all three still have limitations. This study aims to analyze the strengths and weaknesses of these three instruments and develop an alternative assessment tool, the Integrity Assessment Tool (IAT), which can be an alternative choice to the integrity assessment model in Indonesia. This study uses a qualitative descriptive approach through literature studies and analysis of integrity policy documents, with a theoretical foundation of the OECD Integrity Framework and the Anti-Bribery Management System (SNI ISO 37001). The results show that the Integrity Assessment Tool is able to assess organizational integrity comprehensively and objectively through four main dimensions: history, performance, potential, and perception. This approach integrates quantitative, qualitative, and psychometric aspects to produce a more accurate and sustainable picture of the integrity position of public sector organizations. Theoretically, this research enriches the study of public sector integrity management, while practically, it provides guidance for government agencies in building a measurable, systematic, and sustainable integrity evaluation system as a basis for establishing a national model for integrity assessment.

Hydrogen-based hybrid microgrid systems have emerged as a promising solution to enhance renewable energy integration and improve energy supply reliability. By combining renewable sources such as solar and wind with hydrogen production and storage technologies, these systems address the intermittency of renewable power while ensuring continuous energy availability. This study evaluates the techno-economic feasibility, environmental impact, and scalability of hydrogen-based hybrid microgrids, with a focus on cost-effectiveness and system performance under varying operating conditions. Simulation tools, including HOMER Pro and MATLAB Simulink, are used to model the system and conduct sensitivity analyses on hydrogen production costs and demand fluctuations. Key performance indicators such as Levelized Cost of Energy (LCOE), Net Present Value (NPV), and CO₂ emissions reduction are assessed. The results show that although the system requires a high initial investment, it becomes economically viable over time due to reduced operational costs and improved efficiency. Additionally, the system demonstrates significant environmental benefits, outperforming conventional fossil fuel-based systems in terms of emissions reduction. Sensitivity analysis further indicates that advancements in hydrogen production technologies could substantially enhance economic feasibility. Overall, hydrogen-based hybrid microgrids offer a reliable and low-carbon energy solution, supporting sustainable energy transitions and reducing dependence on fossil fuels.

This study aimed to analyze the effect of credit risk on profitability with liquidity as a mediating variable in banking companies listed on the Indonesia Stock Exchange (IDX) during 2022–2024. The study employed a quantitative approach with an explanatory research design. Secondary data were obtained from annual financial statements, and the sample consisted of 31 banking companies selected through purposive sampling from a total of 47 companies. The research variables included credit risk as the independent variable, profitability proxied by Return on Assets (ROA) as the dependent variable, and liquidity proxied by the Loan to Deposit Ratio (LDR) as the mediating variable. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) through the assessment of the measurement model and the structural model. The results indicated that credit risk did not affect profitability and did not affect liquidity, while liquidity affected profitability. The findings also demonstrated that liquidity did not mediate the relationship between credit risk and profitability. The study implied that liquidity management played an important role in supporting bank profitability, whereas the influence of credit risk on profitability during the study period was likely driven by other factors outside the proposed model. This study provided empirical evidence on banking performance dynamics in 2022–2024; however, generalization should have been made cautiously due to the limited observation period and the variables included.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Public service quality has emerged as a crucial metric for assessing how well the government functions and the efficiency of its administration. Recently, rising expectations from the public have led gov-ernments to offer services that are open, responsible, effective, and attuned to citizens' needs. Alongside administrative changes and the evolution of governance models focused on efficiency and citizen satisfaction, the appetite for high-quality public services has surged. Nonetheless, numerous public entities continue to struggle with providing reliable and top-notch services due to a lack of defined service norms and systems for measuring performance. The purpose of this research is to investigate ways to enhance public service quality by establishing robust service standards and quantifiable performance metrics. The study employs a qualitative descriptive methodology, drawing on a review of relevant lit-erature from the past seven years. Multiple academic sources were analyzed to uncover essential ideas, frameworks, and empirical evidence concerning public service management, the enhancement of service quality, and the assessment of performance in governmental organizations. The results show that adopting well-defined service standards, along with measurable performance metrics, can greatly en-hance service effectiveness, accountability, and overall public satisfaction. Additionally, merging prin-ciples of service excellence with quantifiable indicators promotes transparency, boosts organizational performance, and fosters ongoing improvements in service delivery. The research concludes that to enhance public service quality, a structured framework for service standards, ongoing performance assessments, and a firm organizational dedication to innovation and service enhancement are necessary.

Information technology advancements have sparked a digital revolution in village government operations, especially in tracking and assessing village officials' performance. Performance evaluation is still done by hand in many village government organizations, which frequently causes reporting delays, ineffective data processing, and a lack of transparency in the evaluation process. In order to facilitate a more efficient and organized monitoring and performance evaluation process, an information system therefore required. In order to support the management village apparatus data, performance assessment procedures, and integrated reporting, this study intends design and develop a web-based monitoring system for assessing the performance village officials. To represent the system processes in an organized way, the system design is modeled using the Unified Modeling Language (UML), which includes use case diagrams, activity diagrams, and sequence diagrams. To make sure that every system function performs in accordance with user requirements, system testing is carried out using the Black Box Testing method. The findings demonstrate the system's ability handle village apparatus data, carry out automated performance evaluation procedures, compute final scores using assessment indicators, and produce precise performance reports. It is anticipated that the system's implementation will enhance the village officials' performance monitoring procedure's efficacy, efficiency, transparency, and structure.

This community service program aims to enhance teachers’ competence in developing Higher Order Thinking Skills (HOTS)-based assessment items to support improved performance in the Computer-Based National Assessment (ANBK) at SMP Negeri Kletek. The main problem faced by the partner school is the limited understanding of teachers in designing assessment instruments that measure higher-order thinking skills, as well as insufficient ability to develop literacy- and numeracy-oriented questions aligned with ANBK characteristics. This condition has resulted in suboptimal learning evaluation quality and limited student readiness for national assessment. The program employed a Participatory Action Research (PAR) approach, emphasizing the active involvement of participants throughout all stages. The implementation included the planning stage through needs assessment and problem identification, the action stage through training and mentoring in HOTS-based item development, the observation stage through evaluation of participants’ work, and the reflection stage to identify achievements and challenges. Data were collected using pre-test and post-test, observation, and reflective discussions. The results indicate an improvement in teachers’ understanding and skills in developing HOTS-based items, as reflected in higher post-test scores. Teachers were able to design competency-based blueprints, contextual stimuli, and analytical, evaluative, and creative questions. This program effectively strengthens teachers’ professional competence and school readiness for ANBK.

Public service quality serves as a crucial metric for assessing how well the government meets the needs and expectations of the community. Within public administration, the effectiveness of public services indicates the government's capability to deliver governance that is open, responsible, and focused on the citizens. In Indonesia, one tool for gauging public service effectiveness is the Community Satisfaction Index (Indeks Kepuasan Masyarakat/IKM). This research seeks to examine how community satisfaction in public services is measured by identifying various service components and the approach used to compute the IKM. A descriptive qualitative research method is employed, utilizing a literature review. Information was gathered from books, academic journals, government regulations, and other scholarly materials relevant to public service quality and community satisfaction. The results reveal that the IKM measurement system offers a comprehensive framework for assessing the performance of public services based on several aspects, including service requirements, processes, completion time, costs, the skills and conduct of service personnel, and available facilities. Additionally, the findings demonstrate that enhancements in service quality, transparency, and the responsiveness of public organizations greatly affect community satisfaction levels. As a result, ongoing assessments of public services through the IKM framework are vital for improving governance effectiveness, boosting service delivery, and reinforcing public confidence in government agencies.

This research investigates transparency and accountability in the management of state-owned assets (BMN) at the East Java Regional Police's Logistics Bureau as a step toward enhancing effective, adaptive, and integrity-focused governance in police logistics. The research context arises from the growing public call for transparency and accountability in managing state assets, in line with policies for bureaucratic reform and digitalization in the police logistics framework. This study utilized a qualitative descriptive approach involving in-depth interviews, observations in the field, and analysis of documents. The results show that adopting a unified digital system has enhanced the efficiency of asset management, sped up inventory operations, and reduced the risks of asset misuse. Nonetheless, obstacles persist, such as restricted human resource capacity, data alignment problems, and inadequate performance-based assessments. The conversation emphasizes that effective asset management relies on the implementation of adaptive governance, motivation for public service, and precise performance assessment systems. The research indicates that the integration of digital innovation, professional skills, and a robust commitment to public accountability is crucial for creating transparent and sustainable governance in police logistics

In contemporary higher education, English for Specific Purposes (ESP) is increasingly expected to prepare learners for meaningful professional participation rather than isolated linguistic performance. Nevertheless, ESP pedagogy has often remained predominantly skill-oriented, with limited conceptual integration of pedagogical innovations such as project-based learning and digital learning. This study presents a systematic literature review of 40 peer-reviewed studies examining how ESP pedagogy is being reframed as situated professional practice through the integration of project-based and digital learning. Following the process of thematic synthesis, the review provides an examination of what ESP learning is perceived to be, how it is conducted and how it is evaluated in different disciplines and countries. Results show a marked pedagogical transition from a language-centric approach to one that incorporated more authenticity, collaboration and professional concern. Reported learning attainments go beyond language to cover professional capability, digital literacy, autonomous learning and identity development. Project-based learning authenticates language use in professional workflows, and digital learning environments support multimodal communication reminiscent of the way people work today. This review contends that in combination, these perspectives re-conceptualise the pedagogical nature of ESP as a type of applied professional education ’realised’ within particular social, institutional and discipline specific contexts. By synthesizing empirical evidence across settings, the study offers a conceptual reframing of ESP pedagogy and outlines implications for curriculum design, assessment, and future research in higher education and the social humanities.

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems