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This study examines the social and moral values contained in the Indonesian film Jumbo and their relevance as teaching materials for writing drama texts for eleventh-grade senior high school students. The background of this research is the need for contextual and value-based learning resources to support students’ drama writing skills. This study aims to identify social and moral values in the film and explain their applicability in drama writing instruction. A qualitative descriptive method with a literary analysis approach was employed. The data consist of dialogues, scenes, and conflicts in the film, collected through observation and documentation techniques. The findings reveal eighteen data of social values and twelve data of moral values, reflected through character interactions and ethical choices. These values are relevant for developing conflicts, characters, and messages in drama texts. This study concludes that Jumbo can serve as an effective and meaningful teaching material to enhance students’ drama writing skills and support character education.

Evacuation planning in spatial networks requires the identification of critical nodes that maintain connectivity, accessibility, and flow distribution during emergency situations. Existing approaches often rely on individual centrality measures, which capture only a single structural dimension of node importance and may therefore produce incomplete or biased prioritization. To address this limitation, this study proposes a Composite Centrality Framework for identifying critical nodes in meso-scale spatial networks with semi-structured connectivity. The network is modeled as a weighted undirected graph, and Degree, Betweenness, and Closeness Centrality are integrated into a unified composite index to capture complementary structural roles. The framework is implemented in MATLAB and evaluated using a real-world campus spatial network consisting of 30 nodes and a synthetic network comprising 16 nodes with comparable structural characteristics. The results reveal a highly uneven distribution of node importance, with a small set of structurally dominant nodes consistently identified across both networks. In the campus network, node P1 achieves the highest composite centrality score (0.2195) and ranks first across the individual centrality measures, indicating its dominant role in maintaining network connectivity, accessibility, and flow distribution. Quantitative evaluation demonstrates strong agreement between the composite ranking and the individual measures, with Spearman rank correlation coefficients of 0.94, 0.89, and 0.91 for Degree, Betweenness, and Closeness Centrality, respectively. However, only one node (P1) appears simultaneously in the top five of all rankings, highlighting the complementary nature of the individual centrality measures and supporting the need for multi-criteria integration. Sensitivity analysis across three weighting scenarios yields rank correlations exceeding 0.97, confirming ranking stability and methodological robustness. Overall, the proposed framework provides a balanced and reliable approach for identifying critical nodes and demonstrates potential applicability to evacuation planning and spatial network analysis in semi-structured environments.

Drug misuse continues to be a complicated social and legal issue that has an impact on societal stability, public health, and personal wellbeing. Many criminal justice systems have moved away from punitive tactics in favor of rehabilitation-focused ones in recent years, especially for drug users who are frequently viewed as sufferers of addiction rather than serious criminals. The National Narcotics Agency (BNN) in Indonesia is implementing rehabilitation programs and restorative justice processes as a result of this change. This study investigates the efficacy of restorative justice in drug rehabilitation at Jambi Province's National Narcotics Agency and evaluates its applicability from the standpoint of Islamic law, specifically the framework of maqāṣid al-sharīʿah. The study uses a case study design and a qualitative methodology. Participant observation, document analysis, and in-depth interviews with BNN officials, rehabilitation counselors, medical staff, and ex-drug users were used to gather data. The results show that an integrated evaluation system that assesses drug users' physical, psychological, and social states in order to determine their eligibility for recovery is used to institutionally apply restorative justice principles. Combining medical care, psychological counseling, and social reintegration programs, the rehabilitation programs greatly aid in participants' recuperation, enhance psychological stability, and fortify familial ties. Additionally, by promoting individual responsibility, family support, and community involvement, restorative justice-based rehabilitation lowers the risk of recidivism. From the standpoint of Islamic legal philosophy, these actions are consistent with the goals of maqāṣid al-sharīʿah, specifically the defense of human dignity, life (ḥifḍ al-nafs), and intellect (ḥifḍ al-ʿaql). According to the study's findings, restorative justice-based rehabilitation is a compassionate and successful method of treating drug dependency while encouraging social reintegration and long-term recovery.

This study aims to analyze the effectiveness of public service policy implementation at the Regional Technical Implementation Unit of the Freshwater Fish Cultivation Center (UPTD BBIAT) in Pekalongan Regency, focusing on the influence of service quality on fish farmers' satisfaction mediated by trust. The research employs a mixed-methods approach with a concurrent embedded design, combining a survey of 40 fish farmers and in-depth interviews with 5 key informants from the UPTD and Agency levels, along with observation and document analysis. The theoretical framework is built on New Public Service Theory, Policy Implementation Theory, and the SERVQUAL, trust, and public satisfaction concepts. The research findings indicate that all three variables are in the high category with robust and significant positive correlations: service quality with trust, service quality with satisfaction, and trust with satisfaction. Trust has been shown to play a strong mediating role in the relationship between service quality and fish farmers' satisfaction. The assurance and empathy dimensions are the strengths of UPTD BBIAT, while tangibles and reliability require improvement through infrastructure modernization and enhanced consistency in seed availability. Theoretically, this research confirms the applicability of the SERVQUAL model and trust theory in the context of public services in the fisheries sector. In practice, it provides strategic recommendations to improve policy implementation effectiveness through infrastructure strengthening, human resource development, service digitalization, and enhanced institutional coordination.

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL for homomorphic encryption and PySNARK for Zero-Knowledge Proof verification. Experiments are conducted on standard consumer-grade hardware (Intel i5, 8 GB RAM, Ubuntu 22.04) using datasets ranging from 100 MB to 1 GB. The evaluation focuses on encryption and decryption time, homomorphic computation latency, memory usage, and proof generation overhead. Experimental results show that integrating ZKPs introduces a moderate and stable runtime overhead of approximately 15–20%, as analyzed in Section 4, while enabling verification without plaintext disclosure. Ciphertext expansion remains a notable limitation, with observed growth of approximately 30–40× relative to plaintext size, consistent with prior FHE implementations. Despite these overheads, the system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware. Overall, the results indicate that the integrated FHE+ZKP approach provides a practical balance between confidentiality, verifiability, and performance, supporting its applicability to privacy-preserving scenarios such as secure cloud computation, encrypted data analytics, and confidential data processing under realistic resource constraints.

This study investigates the effect of adding onion peel extract as a corrosion inhibitor on the corrosion rate and hardness of radiator pipes. The research employed an experimental method with inhibitor concentrations of 0 ppm, 100 ppm, 200 ppm, and 300 ppm. Corrosion rate testing was conducted using electrochemical methods, while hardness was measured using the Vickers method. The findings reveal that the addition of onion peel extract at a concentration of 300 ppm significantly reduced the corrosion rate to 0.081 mmpy, achieving an inhibition efficiency of 56.45%. Furthermore, the same concentration enhanced the surface hardness of radiator pipes to 255.403 Kgf/mm². These results demonstrate that onion peel extract has strong potential as an eco-friendly organic corrosion inhibitor. Its dual function in reducing corrosion and improving mechanical properties highlights its applicability in radiator pipe protection and sustainable engineering practices. The study contributes to the development of natural inhibitors as alternatives to synthetic chemicals, aligning with environmental preservation efforts and advancing green technology in material protection.

Law Number 11 of 2006 on the Governance of Aceh embodies the principle of lex specialis due to its territorial scope, while Law Number 4 of 2009 on Mineral and Coal Mining also contains a lex specialis character as it specifically regulates the mining sector. This situation raises a legal question regarding how the principles of lex specialis derogat legi generali, lex superior derogat legi inferiori, and lex posterior derogat legi priori should be interpreted and applied by the Government within the framework of Aceh as a Special Autonomous Region. This study aims to conduct an in-depth legal analysis using a progressive law approach to examine whether the principles of lex posterior or lex superior may override the principle of lex specialis as applied in Aceh’s special autonomy regime. The research employs normative legal methods with qualitative analysis. The findings indicate that the principle of lex specialis derogat legi generali as stipulated in the Law on the Governance of Aceh must take precedence due to its specific territorial applicability and special autonomous status. In resolving such normative conflicts, a clear legal framework is required, accompanied by the renewal of legal theories and principles to ensure legal certainty, prevent regulatory overlap, and promote harmony among statutory regulations.

Objective: Autoimmune diseases are complex disorders that arise when the immune system loses tolerance to self-antigens, leading to chronic inflammation and tissue damage. To understand disease pathogenesis and to evaluate therapeutic efficacy, animal models are widely used in autoimmune research. This review aims to analyze various types of animal models employed in studies of autoimmune diseases such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), type 1 diabetes mellitus (T1DM), and multiple sclerosis (MS), with a particular focus on reproducibility and clinical applicability. Methods: This study was conducted through the selection and analysis of scientific literature published over the last ten years (2015–2025), using specific keywords including “clinical application,” “autoimmunity,” “animal models,” “humanized mice,” “lupus,” “rheumatoid arthritis,” “reproducibility,” and “translational research.” Literature searches were performed in major databases such as Google Scholar, PubMed, ScienceDirect, and Scopus. Results: Spontaneous models, such as NOD and MRL/lpr mice, exhibit close resemblance to human disease pathogenesis but are influenced by strain variability and environmental factors. Induced models, including collagen-induced arthritis (CIA) and experimental autoimmune encephalomyelitis (EAE), allow greater control over disease onset but do not fully capture the clinical complexity observed in humans. Humanized models demonstrate high translational relevance; however, their use is constrained by high costs and technical limitations. Conclusion: No single animal model is universally ideal for studying autoimmune diseases. Model selection should be based on biological relevance, reproducibility of outcomes, and the potential for clinical translation in autoimmune disease research.

This research explores the role of human algorithm interaction mechanisms in enhancing trust, reliability, and user confidence in Decision Support Systems (DSS). Traditional DSS models often focus solely on algorithmic accuracy and performance, neglecting crucial factors such as transparency and user engagement, which are essential for building trust. By incorporating explainable AI (XAI) techniques like SHAP and LIME, real-time feedback mechanisms, and user-friendly interfaces, the study develops structured interaction models that improve the interpretability of AI-driven decisions. The results show that transparent decision-making processes and interactive features significantly enhance user trust, making DSS more reliable and easier to adopt. Users interacting with systems that provide clear, understandable explanations of decisions, along with real-time updates on the system’s confidence, reported higher levels of decision-making confidence, especially in high-stakes scenarios. These improvements lead to greater user engagement and adoption of the system in various domains, including healthcare and finance. The study also highlights the importance of balancing interpretability with efficiency in user interface design to ensure both trust and usability. The findings contribute to the design of more user-centric DSS that prioritize trust, interpretability, and cognitive factors, providing a framework for the successful integration of intelligent decision support systems in complex decision-making environments. Future research should focus on refining interaction models and exploring the broader applicability of these systems in different sectors.

The increasing demand for rapid software delivery has led to the widespread adoption of Continuous Integration (CI) and Continuous Deployment (CD) pipelines. These pipelines automate the processes of code integration, testing, and deployment, significantly improving the speed and reliability of software development. However, traditional CI or CD pipelines often overlook security testing, leading to vulnerabilities in the deployed software. To address this gap, this study proposes an integrated framework that embeds automated security testing within the CI or CD process. The framework incorporates security testing tools such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Vulnerability Assessment and Penetration Testing (VAPT) to ensure continuous security checks throughout the development lifecycle. The experimental results show that the proposed framework enhances early vulnerability detection, with detection rates increasing from 30% to 70%. Additionally, the framework reduces deployment failures from 50% to 20%, demonstrating its effectiveness in improving software dependability. While the integration of automated security testing adds a slight 5% increase in pipeline execution time, this minimal impact does not significantly affect the overall speed of the pipeline. The proposed approach successfully balances security and efficiency, ensuring that software is both secure and delivered at high speed. This research highlights the importance of integrating security into CI or CD pipelines and demonstrates that it is possible to achieve high security without sacrificing the speed of software development. The study also discusses the practical implications for software development teams and suggests areas for future research, including the integration of advanced AI-driven security testing tools and the expansion of the framework's applicability across different software projects.

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

This 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.

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

Production planning and inventory control are critical aspects of operations management, as they directly influence cost efficiency, resource utilization, and the continuity of the production process. Ineffective planning and inventory decisions may lead to excessive costs, production delays, or imbalances between supply and demand. The complexity of these problems, which often involve multi-period horizons and multi-stage decision-making processes, has encouraged the application of quantitative optimization methods, one of which is dynamic programming. This study aims to analyze and synthesize the application of dynamic programming in production planning and inventory control through a Systematic Literature Review (SLR) approach. The SLR process was conducted by systematically identifying, selecting, and analyzing 15 relevant national journal articles published between 2015 and 2024 and obtained from various recognized scientific databases. The reviewed literature indicates that dynamic programming is effective in supporting optimal decision-making by determining appropriate production quantities and inventory levels, minimizing total production and holding costs, and managing fluctuating demand conditions. In addition, this method helps reduce the risks associated with overstock and stockouts by considering sequential decision structures. However, the findings also reveal several limitations of dynamic programming, including high computational complexity, strong dependence on deterministic data assumptions, and limited flexibility in handling high levels of uncertainty. These constraints suggest the need for further methodological development or integration with other approaches to enhance practical applicability.

Digital transformation initiatives have become essential for organizations seeking to remain competitive in today’s rapidly evolving technological landscape. However, many organizations face challenges due to ineffective Information Systems (IS) governance, which hampers strategic decision-making and the successful execution of these initiatives. This study aims to develop an IS governance framework that enhances decision-making quality by aligning IT decisions with organizational goals during digital transformation efforts. The proposed framework addresses existing gaps in current IS governance models, offering a solution to common challenges such as inadequate governance structures, resource constraints, and misalignment between IT and business strategies. The framework was developed through a mixed-method approach, including conceptual framework development, expert consensus via the Delphi method, and organizational validation studies. Key findings reveal that the framework improves transparency in decision-making, enhances accountability for IT decisions, and ensures better alignment between IT strategies and organizational objectives. By embedding agile leadership and data-driven decision-making principles, the framework enables organizations to respond effectively to the fast-changing dynamics of digital transformation. This study also compares the proposed framework to existing models such as COBIT and ITIL, highlighting its unique features, including its adaptability to the fluid nature of digital transformation. The framework's strengths include its comprehensiveness and flexibility, though its application may face challenges in organizations with limited digital maturity or rigid governance structures. Future research directions include exploring the integration of emerging technologies into the framework and its applicability across different organizational contexts.

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 evaluate the relationship between skinfold calliper measurements and handgrip strength with segmental fat and muscle composition among adults in Kota Bambu, providing evidence for simple and applicable community-based screening tools. A cross-sectional design was applied to 135 participants aged 18–96 years. Skinfold thickness was assessed at four anatomical sites (biceps, triceps, suprailiac, scapular), handgrip strength was measured using a digital dynamometer, and segmental body composition was obtained via bioelectrical impedance analysis. Findings demonstrated a moderate positive correlation between handgrip strength and arm skeletal muscle mass (r = 0.371–0.407; p < 0.01), indicating that handgrip performance reflects segmental muscle contractility and functional reserve. Skinfold measurements showed moderate-to-strong positive correlations with both local and central subcutaneous fat distribution (r = 0.562–0.635; p < 0.01), confirming their sensitivity in estimating segmental adipose accumulation. These results highlight that calliper and handgrip strength can serve as practical, low-cost preliminary screening indicators for mapping muscle and fat distribution in urban communities, although they are not substitutes for comprehensive body composition assessment. Further longitudinal studies integrating advanced physiological and functional parameters are recommended to enhance predictive validity and clinical applicability.

This research analyzes the application of J. Oswald Sanders' servant leadership concept to tongkonan (traditional house) leadership in Toraja society. The background of this study is the presence of authoritarian leadership tendencies among to parenge (tongkonan leaders) who prioritize personal power over community service and empowerment. This research employs a qualitative approach through literature review, analyzing academic articles, research journals, and theological sources to understand the relevance and applicability of Sanders' principles within the Toraja cultural context. The findings reveal that Sanders' servant leadership, which is grounded in spiritual discipline and community empowerment, represents a viable alternative for reconstructing tongkonan leadership from an authoritarian model to a collegial model. However, implementing this model faces structural challenges due to the transformation of tongkonan's functions and the intrusion of formal government systems. In conclusion, Sanders' principles can be applied to tongkonan leadership through institutional reconstruction that combines traditional Toraja values with transparent and accountable leadership practices, thereby creating leadership that is simultaneously serving and empowering the community.