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Factual verification of political party membership constitutes a critical administrative stage in electoral management, as it directly affects electoral integrity and democratic legitimacy. In Indonesia, this process remains predominantly reliant on conventional door-to-door verification methods, which face structural constraints including time limitations, excessive administrative burden, high costs, and vulnerability to procedural errors and electoral disputes. Meanwhile, the advancement of e-government and Electronic-Based Government Systems (E-Government) has created opportunities to adopt more accountable and verifiable digital identity mechanisms. This study aims to conceptualize and examine Electronic Know Your Customer (E-KYC) as an administrative governance model for verifying political party membership to strengthen electoral integrity within an e-government framework. Employing a mixed-methods approach with a sequential explanatory design, quantitative data were collected through a survey of 44 election officials at district and municipal election commissions in Central Java Province, Indonesia. Qualitative data were obtained through in-depth interviews with key institutional actors. The findings demonstrate a positive and significant relationship between the adoption of E-KYC and strengthened administrative electoral integrity, democratic public service values, and institutional readiness. Qualitative evidence further indicates that challenges to electoral integrity stem primarily from weaknesses in administrative procedures rather than from political contestation itself. This study concludes that E-KYC should be positioned not merely as a technological innovation but as an institutionalized administrative governance model, contingent upon regulatory clarity, system interoperability, and organizational capacity building.

This study aims to demonstrate that a lack of curiosity is a fundamental factor influencing adolescents’ preference for social media over traditional news outlets. To examine this issue, the research employed a mixed-method approach combining quantitative and qualitative data gathered through questionnaires. A total of 30 informants participated and were categorized into two developmental groups: early adolescents aged 12–14 and middle adolescents aged 15–17. The questionnaire explored respondents’ curiosity levels, information-seeking habits, and motivations for choosing certain media platforms. The findings indicate that low curiosity significantly contributes to the preference for social media among early adolescents. At this stage, their interest in deeper information is still limited, making them more inclined toward fast, simple, and entertaining content. However, the hypothesis is less applicable to middle adolescents. Although they also frequently use social media, their media choices are influenced by additional factors such as peer dynamics, a growing sense of autonomy, and broader exposure to information from various sources. This difference suggests that the relationship between curiosity and media preference becomes more complex as adolescents mature. Overall, the study highlights that strategies to encourage critical information-seeking behavior must be tailored to adolescents’ developmental stages.

This research aims to conduct an in-depth analysis of the complex dynamics termed the "Digital Bureaucracy Paradox," a phenomenon that emerged significantly following the implementation of the Work From Anywhere (WFA) policy within the Regional Secretariat of Sorong City. The primary focus this study lies in the strategic dilemma faced by visionary leadership in balancing modern flexible work patterns with the obligation to enforce Civil Servant (ASN) discipline, which has historically been conventional and rigid. Amidst massive digital transformation, local-level bureaucracy is forced to adapt to work models requiring high agility, while simultaneously remaining bound by formalistic disciplinary regulatory standards. Quantitative findings indicate that although digital platforms have been effective as instruments for work instructions, the effectiveness visual supervision remains irreplaceable in maintaining the integrity of working hours, particularly regarding low scores in separating personal and professional matters during WFA. Conversely, submissions the E-Kinerja (E-Performance) system show very high level of administrative compliance, yet do not fully guarantee the quality of substantive outputs. Statistical analysis confirms that adaptive digital leadership has decisive influence on the successful implementation of the Electronic-Based Government System (SPBE). These findings offer  theoretical contribution to the study of bureaucratic behaviour within digital ecosystems and provide practical recommendations for redefining the ASN discipline from formalistic patterns toward a result-based substantive discipline. The synergy between visionary leadership and the strengthening of bottom-up accountability mechanisms through public participation is expected to realise a governance framework that is not only technologically modern but also functionally accountable in the post-pandemic era.

This study aims to analyze the role of marketplace in encouraging digital internationalization and product development at PT Bungas Food Nusantara. The research uses a qualitative approach with a case study method. Data was obtained through in-depth interviews with company management, observation of activities on the marketplace platform, and supporting documentation. The results of the study show that marketplaces function not only as digital distribution channels, but also as strategic infrastructure that allows companies to reach international markets without conventional export mechanisms. Internationalization occurs gradually through increased demand from overseas consumers facilitated by the platform's algorithmic system and global visibility. In addition, the marketplace's reviews, ratings, and analytics features are used as the basis for product development, including packaging adjustments, variant innovation, and data-driven promotional strategies. These findings show that marketplaces play a role as a catalyst for internationalization as well as a driver of product innovation in the context of the digital economy.

This study aims to analyze the effect of information technology (IT)-based learning media on improving students' academic performance. With the advancement of digital technology, the use of IT-based media such as interactive presentations, educational videos, Learning Management Systems (LMS), and online quiz applications has become part of modern teaching strategies. This study uses a quantitative approach with a quasi-experimental method. The subjects of the study were secondary school students divided into experimental and control groups. The instruments used include learning achievement tests to measure academic performance and observation sheets to assess the implementation of IT media usage. Data were analyzed using t-tests and simple regression analysis. The results show a significant difference in academic performance between students who used IT-based learning media and those who used conventional methods. The experimental group showed a higher average score compared to the control group. These findings indicate that the use of IT-based learning media, when planned and implemented systematically, can improve students' motivation, engagement, and understanding of learning materials. Therefore, the integration of information technology into the learning process is recommended as an innovative strategy to enhance the quality of education.

The advancement of information technology has rapidly transformed trading patterns in Indonesia, shifting from conventional transactions to online transactions through marketplace platforms. On one hand, this transformation provides convenience and efficiency for both businesses and consumers. On the other hand, it has also given rise to various legal issues, particularly regarding consumer protection. This article aims to examine how legal protection for consumers is implemented in electronic sales agreements on marketplaces, while also identifying the obstacles encountered during its implementation. The study employs a normative juridical approach, using conceptual analysis and legislative review, supplemented by empirical data obtained from interviews. As described, legal protection for consumers in electronic transactions in Indonesia remains suboptimal. Specifically, these challenges include biased law enforcement, low levels of consumer literacy, and ineffective dispute resolution mechanisms. In practice, marketplaces have incorporated consumer protection features such as escrow systems, refund mechanisms, and complaint centers; however, their implementation still suffers from limited transparency and effectiveness. Furthermore, existing regulations are slow to respond to the dynamics of cross-border transactions and ongoing digital innovations. Therefore, comprehensive regulatory reform, stronger enforcement, and enhanced legal and digital literacy among the public are necessary to ensure effective consumer protection.

Domestic violence (DV) remains a complex legal and social problem, threatening family stability and human dignity. Although Law No. 23 of 2004 concerning the Elimination of Domestic Violence provides strict sanctions, conventional punishment methods are often considered ineffective in restoring damaged social relationships within families. This study examines the implementation of restorative justice in resolving domestic violence cases within the Bungo Police jurisdiction, from the perspective of positive law and Islamic law. This study uses an empirical legal approach and a sociological perspective, with primary data collected through interviews with investigators from the Women and Children Protection Unit (PPA), as well as secondary data from laws, police regulations, and related academic literature. The results show that restorative justice has been applied to several domestic violence cases during the investigation stage through mediation and peace agreements between the parties involved. This method helps reduce the backlog of cases, accelerates dispute resolution, and maintains family social stability. However, challenges remain, such as the possibility of re-victimization due to the imbalance of power between victims and perpetrators. From an Islamic legal perspective, restorative justice is in line with the principles of ta'zīr and maqāṣid al-sharī'ah, especially the preservation of human dignity, life and offspring. By guaranteeing the protection of victims and preventing repeated violence, restorative justice can be an additional mechanism in cases of domestic violence.

This study aims to improve students' understanding of the obligation to serve their parents through the application of mind mapping in the 11th-grade Al-Qur'an and Hadith subject at MA NU Maron. The background of this study was the lack of student understanding of the material presented using conventional methods, necessitating learning innovations that are engaging and motivating. This study used a Classroom Action Research (CAR) approach, implemented in two cycles. Each cycle included planning, implementation, observation, and reflection. The research instruments consisted of comprehension tests, observation sheets, and documentation. The results showed that the application of the mind mapping method increased student engagement in the learning process, facilitated their understanding of the material, and strengthened their memory. In the first cycle, students' learning achievement showed an initial increase, and in the second cycle, the results improved significantly, reaching the specified success indicator. Thus, it can be concluded that the mind mapping method is effective in improving students' understanding of the Al-Qur'an and Hadith subject, particularly the material on the obligation to serve their parents.

Higher education management at the master's level currently faces urgent challenges, namely learning fatigue and low engagement among professional students, especially in Prior Learning Recognition (RPL) classes. Currently, lecturers still tend to apply conventional learning methods based on static presentations that fail to accommodate andragogical characteristics due to a lack of dynamic interaction. Therefore, this study aims to evaluate the effectiveness of the “Humanistic Digital Andragogy” approach through the integration of gamification (Kahoot) and visual thinking (Whimsical) in the Strategic Human Resource Management course. The researchers used a descriptive qualitative design with thematic analysis and collected data through feedback from students in the Master of Management Program (Semarang and Sorong classes). The results revealed that technology served as a double catalyst: Whimsical visualization effectively reduced the cognitive load of complex strategy material, while competition in Kahoot triggered positive adrenaline (eustress) that increased attention. These findings confirm that the success of technology is highly dependent on the role of lecturers as humanistic facilitators (high-touch). This synergy has been proven to change students' perceptions of HRM from merely administrative to strategic partners, as well as creating learning satisfaction that is relevant to the world of work.

Early reading skills are fundamental competencies that must be mastered by elementary school students, as they serve as the foundation for future academic success. However, conventional teacher-centered instruction often fails to maximize students’ active engagement in learning. This study aims to analyze the effectiveness of the Project Based Learning model assisted by mind mapping media in improving the early reading skills of second-grade elementary students. This research employed a quantitative approach with an experimental design. The participants were second-grade students who received instruction through the Project Based Learning model assisted by mind mapping media. Data were collected using reading skill tests administered before and after the treatment. The data were analyzed to identify differences in students’ reading abilities following the implementation of the learning model. The findings indicate that the implementation of Project Based Learning assisted by mind mapping media has a positive effect on students’ early reading skills. The model promotes active student participation, enhances critical and creative thinking skills, and facilitates comprehension through structured visual representation of concepts. Furthermore, the learning environment becomes more interactive and meaningful, increasing students’ motivation to engage in reading activities. Therefore, the Project Based Learning model assisted by mind mapping media is recommended as an alternative instructional strategy to improve early reading skills in elementary schools.

This meta-analytic study aims to comprehensively examine the relationship between mental health, learning capacity among health education students, and competencies in nosocomial disease risk management through cross-contextual empirical synthesis. An analysis of 47 studies involving 12,847 participants from 15 countries demonstrates a strong, statistically significant association between students' mental health and competencies in nosocomial infection prevention, as reflected by a correlation coefficient of r=0.68 (p<0.001) and a 95% confidence interval of 0.61-0.74. Students with high mental health scores (M=78.4; SD=8.2) exhibited substantially superior understanding of infection prevention protocols, namely 43% higher than the control group (M=54.7; SD=12.1; t(846)=18.42; p<0.001; d=2.31). Structural equation modeling confirmed learning capacity as a significant partial mediator (β=0.52; p<0.001), with an indirect effect reaching 35.4% and a 95% CI range of 28.6-42.1%. Mindfulness-based psychoeducational interventions were shown to enhance nosocomial risk identification abilities by 38.7% (F(2,564)=42.18; p<0.001; η²=0.41) while reducing clinical anxiety by 31.2% (t(382)=9.84; p<0.001). These findings extend the frameworks proposed by Song (2024) and Schutte et al. (2025), which primarily emphasize cognitive aspects, by demonstrating that the integration of psychological dimensions yields a multidimensional predictive model explaining 64.3% of the variance in risk management competence (R²=0.643; F(5,841)=304.76; p<0.001), surpassing conventional models that account for only 38-45% of the variance.

The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p < 0.001), privacy (t(95) = 8.93, p < 0.001), and throughput (t(156) = 15.21, p < 0.001). The framework achieved differential privacy with ε = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3× faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.

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.

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 systematically analyzes the effectiveness of school-based resilience programs designed for Palestinian children in the West Bank through a systematic review of 47 programs implemented over the period 2010–2023. The meta-analysis encompasses 12,847 participants aged 6–18 years from 89 schools and demonstrates a significant increase in resilience scores with a large effect size (d = 0.76, p < .001), accompanied by a substantial reduction in psychological trauma symptoms (r = -0.64, p < .001) and marked improvements in academic functioning (β = 0.58, p < .01). Programs integrating mindfulness approaches and psychosocial support exhibited the strongest effects (η² = 0.42), followed by expressive arts interventions (η² = 0.38) and play therapy (η² = 0.35), collectively underscoring the critical importance of non-conventional approaches in contexts of protracted conflict. Multilevel regression analysis revealed that a minimum program duration of 12 weeks and the level of family engagement contributed significantly to enhanced intervention effectiveness (R² = 0.67, p < .001). These findings extend the results of Qouta (2020) and Jabr et al. (2013) on child resilience in conflict zones, while offering an original contribution through the identification of specific program components most responsive to the Palestinian context. In particular, the integration of local cultural elements and the strengthening of collective identity were shown to increase program effectiveness by up to 43% compared with universal approaches, a pattern that has not been systematically documented in the previous literature.

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.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Durian Bhineka Bawor is one of the leading local commodities of Alasmalang Village with high economic potential. However, product promotion and marketing activities are still dominated by conventional methods and limited local networks, resulting in restricted market access, low competitiveness, and the absence of structured product information documentation. This community service program aims to address these challenges through the implementation of an interactive website integrated with an AI Agent, serving as a centralized information platform as well as a digital product ordering service. The main objectives of this program are to strengthen local durian branding through the utilization of modern digital technology, expand market reach, and enhance community digital literacy. The implementation method was carried out in several stages, including program coordination and socialization, content needs assessment, website design and development, AI Agent and WhatsApp server integration, system testing, manager training, official deployment, and continuous assistance. The AI Agent provides interactive services in the form of product information delivery, personalized recommendations, and order facilitation directly connected to the admin dashboard and social media platforms, thereby accelerating transaction processes and improving consumer experience. The expected outcomes of this program include the establishment of a more professional, transparent, and efficient promotion and ordering system for Durian Bhineka Bawor products. The developed platform is expected to expand market access, increase product sales, and create new digital-based business opportunities. Furthermore, this program contributes to community empowerment by improving digital skills and technology management capabilities to support local economic independence and competitiveness.

This study aims to: (1) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the mathematical concept comprehension and self-efficacy of 10th-grade students at SMA Negeri 3 Tarakan; (2) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the mathematical concept comprehension of 10th-grade students at SMA Negeri 3 Tarakan; (3) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the self-efficacy of 10th-grade students at SMA Negeri 3 Tarakan. The research population included all tenth-grade students of SMA Negeri 3 Tarakan in the 2024/2025 academic year. The research sample consisted of two classes selected randomly: one experimental class receiving discovery learning with an aptitude treatment interaction strategy and one control class receiving conventional learning. The research instruments consisted of a test measuring mathematical concept understanding on trigonometry material and a self-efficacy questionnaire. The data obtained were tested for prerequisites through normality and homogeneity tests before being analyzed using inferential statistical tests in the form of an independent samples t-test with the assistance of SPSS software version 26.0. The research results show that the implementation of the discovery learning model with the aptitude-treatment interaction strategy has a significant impact on students' mathematical concept understanding and self-efficacy simultaneously, with a significance value of 0.006 < 0.05. Partially, this learning model has a significant effect on students' mathematical concept understanding, with a significance value of 0.018 < 0.05. However, the effect of the discovery learning model with the aptitude-treatment interaction strategy on students' self-efficacy is not statistically significant, as indicated by a significance value of 0.089 > 0.05, even though there is a tendency for increased self-efficacy among students participating in the experimental class learning. Nevertheless, the influence of the discovery learning model with the aptitude treatment interaction strategy on students' self-efficacy is not statistically significant in partial terms, although there is a tendency for an increase in self-efficacy among students participating in the experimental class. These findings suggest that the discovery learning model with the aptitude treatment interaction strategy is effective in improving students' understanding of mathematical concepts in trigonometry material and has the potential to support the development of self-efficacy in mathematics learning.

This research investigates thermal material properties and performance characteristics for high-speed vessel components subjected to extreme thermal stress during sustained high-speed operations. High-speed vessels including patrol boats, fast ferries, and naval craft experience elevated thermal loads from high-power density propulsion systems, aerodynamic heating, and sustained operational intensities creating demanding conditions for structural and mechanical components. Through qualitative analysis involving naval architects, materials engineers, high-speed vessel operators, and component manufacturers, this study examines how material thermal properties affect component durability, performance, and safety while identifying optimal material selections for critical applications. Results demonstrate that advanced thermal materials including high-temperature aluminum alloys, titanium alloys, ceramic composites, and thermal barrier coatings can extend component service life by 40-70%, improve thermal management effectiveness by 25-45%, and enhance operational reliability compared to conventional materials. Key implementation challenges include material cost premiums of 150-300%, manufacturing complexity, limited operating experience, qualification testing requirements, and supply chain constraints. Findings reveal that strategic thermal material selection for critical components represents essential enabling technology for high-speed vessel performance, reliability, and operational availability supporting defense, commercial, and emergency response applications requiring sustained high-speed capabilities. This research contributes to marine materials engineering literature by providing evidence-based frameworks for thermal material selection applicable to diverse high-speed vessel applications.