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Official travel constitutes a routine yet strategically significant component of local government administration, closely intertwined with bureaucratic processes, public financial management, and accountability arrangements. Despite its operational importance and fiscal visibility, official travel management has received limited integrative attention in the public administration literature, and existing studies remain fragmented across procedural, financial, and governance perspectives. This article addresses this gap by providing a comprehensive literature review on administrative efficiency in official travel management within local governments, with particular attention to bureaucratic processes and cost control mechanisms. Adopting a narrative–analytical literature review design, the study employs a state-of-the-art and theory-driven synthesis of recent peer-reviewed scholarship in public administration, public financial management, governance, and related fields. The analysis integrates thematic and conceptual synthesis techniques to identify recurring patterns, relationships among key concepts, and unresolved issues in the literature. The findings reveal consistent patterns of procedural inefficiency, including administrative burden, complex approval chains, and process fragmentation, which persist even under formal cost control and accountability systems. The review further demonstrates that compliance-oriented financial controls often secure fiscal conformity without necessarily improving administrative efficiency, particularly when misaligned with bureaucratic workflows and constrained by limited administrative capacity. Governance and accountability mechanisms enhance transparency and oversight but frequently prioritize answerability over performance learning, thereby legitimizing inefficiencies rather than resolving them. By synthesizing insights from Administrative Efficiency Theory, Public Financial Management, Bureaucratic Process Theory, Administrative Capacity Theory, and Governance and Accountability perspectives, this article advances an integrative conceptual framework that explains efficiency outcomes as systemic products of interacting institutional dimensions.

The Papua Special Autonomy Fund (Dana Otonomi Khusus Papua) represents a key instrument of Indonesia’s asymmetric fiscal decentralization aimed at reducing historical inequalities, accelerating regional development, and promoting social justice for Indigenous Papuans. However, after more than two decades of implementation, concerns persist regarding its effectiveness in producing equitable welfare outcomes, particularly with respect to accountability, targeting accuracy, and distributive justice. This literature review critically examines existing scholarly research on the governance, implementation, and impacts of Dana Otsus Papua, with an emphasis on how institutional arrangements shape policy performance and equity outcomes. The study employs a narrative–critical literature review enriched with systematic elements, including transparent search procedures, explicit inclusion and exclusion criteria, and thematic synthesis. Peer-reviewed journal articles and reputable conference proceedings were analyzed using thematic analysis and conceptual mapping to identify dominant findings, methodological approaches, and research gaps. The synthesis reveals recurring patterns across the literature. Accountability mechanisms remain fragmented and weakly integrated across planning, budgeting, monitoring, and evaluation processes. Targeting accuracy is inconsistent, with fiscal benefits frequently failing to reach Indigenous Papuans as intended. Moreover, distributive justice outcomes depend more on institutional recognition, participation, and governance capacity than on the size of fiscal transfers alone. The review also highlights a critical gap in integrative evaluations that link governance arrangements, implementation processes, and equity outcomes. The article concludes that improving Dana Otsus Papua requires a shift from expenditure-focused assessments toward governance- and justice-oriented evaluation frameworks. The study contributes theoretically by integrating accountability, implementation, and distributive justice perspectives, and offers practical insights for strengthening oversight, refining targeting mechanisms, enhancing participatory governance, and embedding digital tools within accountability systems.

This study investigates the factors influencing export performance in five ASEAN countries Indonesia, Malaysia, the Philippines, Singapore, and Thailand during the 2014-2023 period. The topic is highly relevant given the vital role of exports in sustaining monetary stability and promoting long-term economic growth. The novelty of this research lies in its integrated approach, which simultaneously examines key export-related macroeconomic variables, namely foreign direct investment and inflation, while incorporating foreign exchange reserves as a moderating variable an approach that remains limited in existing ASEAN-focused studies. This analysis uses secondary data obtained from the World Bank and processed using panel data regression methods, including the Common Effect Model, Fixed Effect Model, and Random Effect Model, strengthened by a Moderated Regression Analysis (MRA) approach. The results show that foreign direct investment and inflation significantly influence foreign exchange reserves. Furthermore, foreign exchange reserves have been shown to play a strategic role in strengthening the economic resilience of ASEAN countries and can be used as a reference in formulating monetary and international trade policies.

The assessment of a company's value is crucial for investors to identify its prospects and performance. Financial ratios such as the Current Ratio (CR) and Return on Assets (ROA) are used to analyze factors affecting the company's value. This study aims to analyze the impact of CR and ROA on company value in manufacturing companies within the Miscellaneous Industries sub-sector for the period 2015–2024. The study uses a quantitative approach with data from annual financial reports of companies listed on the Indonesia Stock Exchange. Data analysis is conducted using panel data regression with the Random Effect Model (REM) as the best model. The dependent variable is company value, measured by Price to Book Value (PBV), while the independent variables consist of CR and ROA. The results show that CR does not have a significant effect on company value, while ROA significantly affects company value. Simultaneously, CR and ROA are proven to significantly affect company value, indicating that the combination of liquidity and profitability plays an important role in explaining PBV variations. This finding suggests that investors pay more attention to profitability than liquidity in the Miscellaneous Industries sector.

This study aims to analyze bureaucratic transformation and public service innovation in the dynamics of the administrative capacity of the Dili City government, specifically at the Cristo Rei District Office, in realizing responsive, accountable, and quality public services. The research approach employed a mixed methods approach, collecting quantitative data through questionnaires from 12 respondents and qualitative data through in-depth interviews with four key informants: Fernando Araujo (District Head), Marciana de Jesus Soares (Head of Administration and Finance), Jose Sarmento (Head of Program Planning), and Ernesto Mendonça (Head of Public Relations). Statistical analysis showed that institutional capacity had a very strong influence on public accountability (r = 0.806; p = 0.002), while bureaucratic responsiveness had a very strong influence on the quality of public services (r = 0.727; p = 0.007). The interviews revealed concrete practices of bureaucratic transformation, such as effective internal coordination, orderly administrative procedures, one-stop service, and responsiveness to citizen needs. This study indicates that institutional capacity and bureaucratic responsiveness are the dominant factors in improving service quality, while formal accountability needs to be made more open and participatory. The findings support the Theory of Administrative Capacity and the New Public Service, and offer recommendations to strengthen participatory mechanisms and make performance evaluation more transparent.  

Employee performance is an important factor in supporting the success of public services, especially in government agencies that deal directly with the public. Efforts to improve employee performance need to be supported by the implementation of good work discipline and the implementation of job training that is appropriate to employee needs. This study aims to analyze the effect of work discipline and job training on employee performance at the Regional Tax Collection Service Unit (UPPPD) of West Jakarta. This study uses a quantitative method, data collection techniques are carried out by distributing questionnaires to employees with a population of 78 respondents. Sampling uses a total sampling technique. Data analysis was carried out using SPSS version 26 through multiple linear regression tests, t-tests, F-tests, and coefficient of determination (R²) tests. The results of the study indicate that work discipline has a positive and significant effect on employee performance with a significance value of 0.000 <0.05 and a calculated t-value of 11.577. Job training also has a positive and significant effect on employee performance with a significance value of 0.000 <0.05 and a calculated t-value of 11.368. Simultaneously, work discipline and job training significantly influence employee performance, with a calculated F-value of 132.931 and a significance level of 0.000 < 0.05. The coefficient of determination indicates that these two variables explain 78.0% of the variation in employee performance (R² = 0.780), while the remaining 22.0% is influenced by other variables outside this study.

The most valuable asset of a company is its employees due to their performance. The performance of PT. XYZ in recent times has tended to decline due to the heavy workload on Group A non-organic employees, which has resulted in reduced employee productivity. This study aims to determine the workload value and the level of fatigue experienced by Group A non-organic employees at PT. XYZ using the SOFI and SDS methods. It also aims to recommend efforts to reduce the workload and stress levels for Group A non-organic employees at PT. XYZ. This study is a descriptive qualitative research using interview methods and distributing questionnaires to 18 non-organic employees of group A. The results of the study indicate that the analysis of workload (fatigue) measurement using the SOFI method obtained a physical fatigue level among non-organic employees of group A at PT. XYZ with an average total score of 4.61, which means that the employees experienced a workload (fatigue) level categorized as moderate. Meanwhile, the analysis of work stress measurement using the SDS method obtained an average total score of 14, indicating that the stress level among non-organic employees of group A at PT. XYZ falls in the moderate category.

The most valuable asset of a company is its employees due to their performance. The performance of PT. XYZ in recent times has tended to decline due to the heavy workload on Group A non-organic employees, which has resulted in reduced employee productivity. This study aims to determine the workload value and the level of fatigue experienced by Group A non-organic employees at PT. XYZ using the SOFI and SDS methods. It also aims to recommend efforts to reduce the workload and stress levels for Group A non-organic employees at PT. XYZ. This study is a descriptive qualitative research using interview methods and distributing questionnaires to 18 non-organic employees of group A. The results of the study indicate that the analysis of workload (fatigue) measurement using the SOFI method obtained a physical fatigue level among non-organic employees of group A at PT. XYZ with an average total score of 4.61, which means that the employees experienced a workload (fatigue) level categorized as moderate. Meanwhile, the analysis of work stress measurement using the SDS method obtained an average total score of 14, indicating that the stress level among non-organic employees of group A at PT. XYZ falls in the moderate category.

Insomnia is a sleep disorder frequently experienced by college students and can negatively impact health and cognitive function, particularly concentration in studies. Prolonged sleep disturbances lead to fatigue, decreased attention, and decreased academic performance. Therefore, this study is important to determine the relationship between insomnia and concentration levels in college students. This type of research is descriptive quantitative using a cross-sectional approach with 100 respondents. Data collection used a questionnaire. Data analysis test used Spearman Rho. The results of univariate analysis, the most insomnia is mild insomnia as many as 53 students with a percentage (53%). The most students with a moderate level of learning concentration are 67 students with a percentage (67%). The most data respondents with female gender as many as 84 with a percentage (84%). The age of the most respondents is 20 years as many as 57 students with a percentage (57%). The results of bivariate analysis with Spearman rho obtained significant results between insomnia and the level of learning concentration of 0.000, the value is <0.005 with a moderate relationship as evidenced by the r value of 0.469. There is a relationship between insomnia and learning concentration levels.

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.

This study aims to determine the leadership strategies of school principals in improving the performance and discipline of teachers at Madrasah Ibtidaiyah Salafiyah Syafiiyah Hadirul Ulum Tasikrejo Pemalang, Central Java, as well as to identify the supporting and inhibiting factors when these leadership strategies are implemented. This study uses field research with a descriptive approach. The data collected was verbal data or words rather than numbers. The location of this research was at MI Salafiyah Syafi'iyah Hadirul Ulum Tasikrejo, Pemalang Regency. The informants in this study were the principal and classroom teachers. The data collection techniques used were observation, interviews, and documentation. The results of the study found by the researcher regarding the principal's strategy in improving teacher performance and discipline at MI Salafiyah Syafi'iyah Hadirul Ulum Tasikrejo, Pemalang Regency, are as follows: (a) conducting supervision (b) setting an example (c) motivating teaching staff (d) providing adequate facilities and infrastructure (e) providing opportunities to attend training or education (f) conducting evaluations. Meanwhile, the supporting and inhibiting factors are divided into several parts. First, there are two supporting factors, namely (a) a sense of togetherness and kinship within the school environment, and (b) the principal's commitment to improving school quality. Meanwhile, there are two inhibiting factors, namely (a) the suboptimal time management of teachers and (b) the work environment. Based on the results of this study, it can be concluded that the principal's leadership strategy plays a very important role in improving the performance and discipline of teachers at MI Salafiyah Syafi'iyah Hadirul Ulum Tasikrejo, Pemalang Regency. The implementation of appropriate strategies, supported by a sense of togetherness and the principal's commitment, can have a positive impact on teacher professionalism. However, there are still several inhibiting factors that need attention so that the implementation of leadership strategies can run more optimally.

This study presents a Graph-Augmented Isolation Forest (GAIF), an unsupervised anomaly-detection framework for analyzing mobile user behavior. The proposed framework represents users and behavioral attributes as a user–feature bipartite graph, enabling the capture of relational dependencies that are not explicitly modeled in conventional vector-based approaches. Low-dimensional user representations are learned through Node2Vec and Graph Sample and Aggregate (GraphSAGE), and the resulting embeddings are subsequently processed by an Isolation Forest to produce anomaly scores. Experiments are conducted on a Mobile Device Usage and User Behavior dataset comprising 700 user profiles derived from application-level behavioral indicators. The dataset is treated as a behavioral abstraction rather than as a malware classification benchmark. A consistent 80:20 stratified train–test split is employed, with all learning-capable operations restricted to the training data to mitigate information leakage. Detection performance is evaluated post hoc using precision, recall, F1-score, and area under the curve (AUC) metrics. Under the evaluated setting, GAIF achieves an F1-score of 0.94 and an AUC of 0.97, demonstrating improved anomaly detection effectiveness relative to representative unsupervised baseline methods. These results are obtained on a static, proxy dataset and should not be interpreted as evidence of real-time deployment capability. Model interpretability is supported through post-hoc Uniform Manifold Approximation and Projection (UMAP) visualizations of the learned embeddings, providing structural insights into anomalous user behavior. Overall, the findings indicate that integrating graph-based representation learning with isolation-based anomaly scoring constitutes a computationally efficient approach for unsupervised mobile user behavior anomaly detection within the scope of this study.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

Prompt injection has emerged as a critical security threat for Large Language Models (LLMs), exploiting their inability to separate instructions from data within application contexts reliably. This paper provides a structured review of current attack vectors, including direct and indirect prompt injection, and highlights the limitations of existing defenses, with particular attention to the fragility of Known-Answer Detection (KAD) against adaptive attacks such as DataFlip. To address these gaps, we propose a novel, hybrid, multi-layered detection framework that operates in real-time. The architecture integrates heuristic pre-filtering for rapid elimination of obvious threats, semantic analysis using fine-tuned transformer embeddings for detecting obfuscated prompts, and behavioral pattern recognition to capture subtle manipulations that evade earlier layers. Our hybrid model achieved an accuracy of 0.974, precision of 1.000, recall of 0.950, and an F1 score of 0.974, indicating strong and balanced detection performance. Unlike prior siloed defenses, the framework proposes coverage across input, semantic, and behavioral dimensions. This layered approach offers a resilient and practical defense, advancing the state of security for LLM-integrated applications.

Artificial Intelligence (AI) is increasingly recognized as a transformative enabler of early disease detection, with the potential to improve diagnostic accuracy, support predictive risk stratification, and advance preventive healthcare. Despite rapid methodological progress, many existing reviews remain performance-centric, offering limited insight into generalizability, ethical governance, and real-world implementation constraints. This paper presents a narrative and integrative review with an adoption-focused, translational perspective, synthesizing recent developments in AI-driven early disease detection across oncology, cardiology, neurology, and infectious disease surveillance. Drawing on peer-reviewed literature published primarily between 2016 and 2025, the review examines reported performance gains alongside persistent limitations related to data heterogeneity, population bias, explainability, and regulatory fragmentation. Through cross-sectional synthesis, we identify three recurring gaps in prior reviews: (i) overgeneralization of AI’s diagnostic superiority, (ii) insufficient consideration of ethical and legal accountability, and (iii) a lack of actionable guidance for scalable clinical implementation. Integrating technical, ethical, and policy dimensions into a unified conceptual framework, this review demonstrates that while AI systems can consistently enhance diagnostic accuracy and early risk stratification in well-defined tasks, sustained clinical adoption depends on aligning technical performance with governance readiness, interpretability, and workflow integration. The analysis further highlights how implementation mechanisms—such as explainable AI, continuous post-deployment monitoring, and clinician-centered deployment strategies—mediate the translation of algorithmic innovation into real-world healthcare impact. Overall, this review provides a critical reference for researchers, clinicians, and policymakers seeking to translate AI innovation into safe, equitable, and trustworthy clinical practice.

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.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

This study explores the principal’s leadership strategies and the role of organizational climate in improving teacher work discipline. Teacher discipline is a crucial element in ensuring instructional quality, professional accountability, and sustainable school performance. A qualitative case study approach was employed to obtain an in-depth understanding of leadership practices implemented in the natural setting of the school. Data were collected through in-depth interviews, participatory observations, and document analysis involving the principal, vice principals, and teachers. The findings reveal that discipline improvement is shaped by exemplary leadership, dialogic communication, continuous coaching, and the application of humanistic regulations supported by moral and social rewards. In addition, a collegial and supportive organizational climate strengthens collective awareness and encourages teachers to comply with professional standards voluntarily. Discipline is therefore constructed not merely through administrative control but through shared values and social interaction. This study contributes to educational leadership literature by providing contextual evidence of how leadership practices interact with organizational environments in shaping teacher behavior. Practically, the findings offer guidance for school leaders in designing participatory and sustainable discipline development strategies.

Low financial literacy, weak sharia-based governance, and limited business legality remain key barriers to the sustainability of Micro, Small, and Medium Enterprises (MSMEs). These constraints reduce managerial capability, restrict access to formal financing, and hinder business growth. This study developed an integrated mentoring model combining financial literacy, sharia governance, and business legalization using a Participatory Action Research (PAR) approach. 25 MSMEs in Brebes Regency participated in four stages: needs assessment, training, mentoring, and evaluation. Data were collected through pre- and post-tests, bookkeeping observations, and legality checklists. Results showed significant improvements: financial literacy scores increased from 52 to 84 (61.5%), bookkeeping adoption rose from 20% to 88%, and Business Identification Number (NIB) ownership increased from 32% to 84%. Average monthly turnover also grew by 33%. These findings indicate that participatory and practice-based mentoring effectively enhances knowledge, behavior, and economic performance. The model offers a scalable strategy for strengthening sustainable and ethical MSME management.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.