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This study discusses a student concentration detection system using Convolutional Neural Network (CNN) with the MobileNetV2 architecture. The dataset was adapted from Classroom Student Behaviors and mapped into four concentration categories: highly focused, focused, less focused, and unfocused. The system was tested with a 720p webcam and produced real-time detection data. The evaluation results show an overall accuracy of 75.85%, with the highest precision achieved in the focused class (0.9859) and the highest recall in the highly focused (0.9739) and unfocused (0.9811) classes. The confusion matrix indicates that the focused class was detected most consistently, while highly focused and unfocused classes were often misclassified as focused, resulting in lower precision. In real-time testing, the system operated at an average of 7 FPS and worked optimally when students faced the camera directly with sufficient lighting, but its performance decreased significantly at face angles greater than 45°. User evaluation shows that 75% of students rated the detection results as accurate/very accurate with an average satisfaction score of 3.6 out of 5, and 75% felt assisted in recognizing their concentration level. From the teachers’ perspective, most stated that the results were consistent with classroom observations, and all expressed willingness to reuse the system.

This research examines digital charity practices within Christian media communication on YouTube, focusing on two distinct donation formats: marapthon live stream donations (e.g., 24‑hour fundraising events) and sermon‑based donations (offerings collected during or after online worship services). Despite the rapid growth of faith‑based online giving, a critical problem remains: the absence of an integrated system that aligns these two donation models with Christian values of transparency, accountability, and community stewardship. Existing platforms often treat live marapthon and sermon donations separately, leading to fragmented donor experiences and inefficient fund utilization. Therefore, this study aims to develop a conceptual framework for an integrated digital charity system by comparatively analyzing media communication strategies in both donation contexts. The proposed method is a netnographic comparative analysis, involving systematic observation of YouTube comments, chat logs, and video descriptions from 10 Christian channels (5 marapthon‑focused, 5 sermon‑focused) over six months, supplemented by semi‑structured interviews with content creators and donors. The main findings reveal that marapthon donations emphasize urgency and real‑time social proof, while sermon donations rely on theological framing and pastoral trust. The synthesis proposes a hybrid system architecture incorporating real‑time donation tracking, automated acknowledgment, and weekly theological reflection modules. In conclusion, integrating both models into a single development framework enhances donor engagement and aligns digital charity with Christian communication ethics, offering practical guidelines for church‑based YouTubers and platform developers.

This study examines disaster management strategies in earthquake-prone countries, with a comparative focus on Japan and the Philippines as case studies for lessons applicable to public administration systems worldwide. Using a qualitative comparative analysis approach, the research evaluates institutional frameworks, policy instruments, community engagement mechanisms, and intergovernmental coordination systems deployed in both countries. Japan’s highly centralized yet locally adaptive Disaster Management Basic Act framework is contrasted with the Philippines’ decentralized National Disaster Risk Reduction and Management (NDRRM) system. Findings reveal that effective disaster management hinges on five critical pillars: strong legal frameworks, inter-agency coordination, investment in early warning systems, community resilience programs, and post-disaster recovery governance. The study further identifies that public trust, administrative capacity, and fiscal decentralization significantly influence disaster response outcomes. Lessons drawn from both countries offer practical recommendations for developing nations seeking to strengthen their disaster governance architectures. This research contributes to the growing body of knowledge on comparative public administration and disaster risk reduction, underscoring the imperative of integrated, adaptive, and community-centered governance frameworks in seismically active regions.

The implementation of smoking prohibition policies in Islamic boarding schools continues to depend largely on manual monitoring methods, which often face challenges related to consistency and supervision range. This study aims to design an Internet of Things (IoT)-based cigarette smoke detection system as an alternative monitoring approach that is more effective, measurable, and sustainable. The system design combines an MQ-2 gas sensor with a NodeMCU ESP8266 microcontroller programmed through the Arduino IDE platform. When smoke levels detected by the sensor exceed the predetermined limit, the system automatically triggers a buzzer and LED as warning indicators while simultaneously sending monitoring data to cloud-based platforms such as Firebase or ThingSpeak for real-time observation through web interfaces. The research outputs include a comprehensive system design consisting of system architecture, electronic circuit schematics, flowcharts, and pseudocode that are systematically arranged to support future prototype development and implementation. Through this design, the proposed system is expected to provide an initial technological solution that can enhance the effectiveness of monitoring and enforcing smoke-free regulations within Islamic boarding school environments.

The proliferation of online gambling promotional comments on Indonesian social media has become a serious issue requiring fast and accurate automated handling. This study aims to implement a Hybrid Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) method to classify online gambling comments and compare its performance with standalone RNN and LSTM models. The research utilized a dataset of 10,230 comments subjected to comprehensive preprocessing stages, including the normalization of non-standard language using a slang dictionary. Testing was conducted across three data-splitting scenarios: 90:10, 80:20, and 70:30. Experimental results demonstrate that the standalone LSTM model achieved the highest average accuracy of 97.45%. However, the Hybrid RNN–LSTM model showed significant superiority in terms of performance stability, yielding the lowest standard deviation (0.0027) and the smallest Coefficient of Variation (0.28%) across all scenarios. These findings indicate that while the LSTM architecture is highly effective at capturing short-text context, the Hybrid approach provides better robustness against fluctuations in data proportions, making it highly relevant for implementation as an automated detection system on social media.

The architecture of consumer decision-making has completely changed due to the quick development of recommendation systems based on artificial intelligence (AI). The majority of earlier studies saw algorithms as instruments for forecasting and maximizing preexisting preferences. This study, however, makes a different claim: algorithmic curation actively shapes preferences rather than just reflecting them. This study creates and evaluates a structural model that examines the impact of algorithmic curation intensity on perceived search autonomy, identity resonance, affective evaluation, and the development of initial preferences. The model is based on identity-based consumption theory and the literature on human-AI interaction. The study's findings, which are based on survey data from Generation Z consumers and Structural Equation Modeling (SEM) analysis, demonstrate a contradictory dynamic: algorithmic curation improves identity resonance and directly influences initial preferences while simultaneously decreasing feelings of autonomy. The primary mediating mechanism that links algorithmic exposure to emotional assessment and preference creation is identified as identity resonance. In addition to introducing the concept of algorithmic consumer formation as a new conceptual framework for comprehending consumer behavior in the AI-based digital era, our findings expand the notion of bounded rationality toward algorithmically bounded agency.

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

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

The money market plays a vital role in the global financial architecture as a provider of short-term liquidity and a primary channel for monetary policy transmission. This research is motivated by the rapid transformation of financial instruments, which now encompass conventional and Sharia-compliant sectors, as well as digital innovations such as e-money and stablecoins. The purpose of this study is to examine the concept of the money market, identify the diversity of modern instruments, and analyze their strategic role in economic stability through a qualitative literature review approach. The analysis shows that the money market is highly effective in managing bank cash reserves and controlling inflation by regulating the money supply. The presence of digital instruments has been proven to accelerate liquidity flows, while Sharia schemes provide transparent and equitable investment alternatives. However, the emergence of digital assets also brings challenges of volatility that require adaptive regulation and professional skepticism from market participants. The implications of this research emphasize the importance of synergy between monetary authorities and financial technology to address global disruption. Strengthening regulations on future instruments is expected to create a more inclusive and stable financial system that can respond precisely to economic shocks.

The increase in the number of elderly residents demands the provision of residential facilities that not only meet physical needs, but also support the psychological and social well-being of their users. This study aims to formulate the concept of behavioral architecture-based nursing home design by focusing on the relationship between elderly activity patterns, privacy levels, and spatial relationships of space in the local context of Indonesia. This study uses a qualitative-descriptive approach in the framework of architectural design, with data collection through observation of elderly activities, site analysis, and documentation studies. Activity data was analyzed to identify space needs and usage patterns, then synthesized with site characteristics to formulate the concepts of zoning, circulation, and behavior-based spatial relationships. The results of the study show that the activities of the elderly form a layered behavioral structure, including residential and health activities as primary needs as well as social, productive, and educational activities as support for psychosocial welfare. Hierarchically arranged space zoning based on privacy levels has been proven to improve the readability of the space, sense of security, and comfort of the elderly. The integration of green open spaces as part of the activity system also strengthens support for light physical activity and social interaction. This study confirms that the application of behavioral architecture allows the translation of data on elderly behavior and site conditions into a contextual, humanist, and quality-of-life-oriented design concept. These findings provide practical implications for designers and policymakers in the development of sustainable elderly housing.

The implementation of the Global Minimum Tax (GMT) through Pillar Two of the OECD/G20 marks a fundamental change in the international tax architecture, especially for developing countries such as Indonesia. One of the key instruments in Pillar Two is the Qualified Domestic Minimum Top-Up Tax (QDMTT), which provides an opportunity for source countries to retain the right to tax the profits of multinational companies with an effective tax rate below 15 percent. This study aims to analyze Indonesia's readiness to face the implementation of GMT through the QDMTT policy, focusing on regulatory aspects and tax administration capacity. The research method uses literature studies with a qualitative-descriptive approach through the analysis of policy documents, tax regulations, as well as academic literature and international reports. The results of the study show that Indonesia's readiness is still in the transition stage. In terms of regulation, Indonesia has shown an initial commitment through the issuance of PMK Number 136 of 2024, but the regulation still needs to be strengthened at a higher level of regulation for long-term legal certainty. From the administrative aspect, the main challenges include the complexity of calculating jurisdiction-based Effective Tax Rates, cross-border data management, as well as increasing the capacity of human resources and information technology infrastructure. This study concludes that the success of QDMTT implementation in Indonesia depends on strengthening regulations, increasing tax administration capacity, and reformulating sustainable investment policies.

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.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

The development of multimedia-based interactive games requires a system capable of effectively managing game logic, character behavior, and the integration of visual and animation elements. This study aims to implement GDScript in the development of a 2D RPG game using the Godot Engine. The research method was carried out through the design of scene and node structures, the implementation of game logic using GDScript, and the application of Finite State Machine (FSM) to regulate enemy behavior. GDScript is used to control character movement, animation systems, and interactions between players and objects in the game. The implementation of FSM allows enemies to have dynamic behavior through state settings such as idle and wander. Functional testing results show that the game system can run according to the design and is capable of producing responsive interactions. In addition, the use of modular architecture in the Godot Engine facilitates system development and maintenance. Based on the research results, the Godot Engine and GDScript are considered effective for developing multimedia-based interactive games.

International student enrollment is a critical driver of financial sustainability for Higher Education Institutions (HEIs). While advanced forecasting is standard in the corporate sector, its application in educational planning remains limited. This study addresses this gap by comparing the predictive performance of ARIMA, Facebook Prophet, and Long Short-Term Memory (LSTM) models. Using a publicly available annual dataset from a US-based institution (2000–2022), the analysis employed a strategic partition training on 2000–2017 and testing on 2018–2019 to validate models on stable, pre-pandemic data. Empirical results revealed that the statistical ARIMA (2,1,0) model demonstrated superior accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 1.26%. Conversely, Prophet (11.81%) and LSTM (13.84%) struggled with the limited sample size, failing to generalize effectively compared to the linear approach. The findings suggest that for annual enrollment trends, parsimonious statistical models outperform complex deep learning architectures, providing administrators with a robust, accessible framework for data-driven strategic decision-making.

This systematic review maps how innovation, technology adoption, and Islamic entrepreneurial behaviors are intertwined and contribute to the SDGs. Searches in Scopus followed PRISMA 2020: of the 166 initial records, 46 were eliminated prior to screening; 120 filtered by title–abstract; 45 read in full; and 25 articles were analyzed in depth. Four RQs lead the synthesis: the form of innovation/adoption (RQ1), impact on behavior and performance (RQ2), and their relationship to the SDGs (RQ3). The findings show five complementary faces of innovation: (1) process-organization (knowledge management, open innovation; innovation capability), (2) sharia business/finance models (sharia venture capital, agricultural value chain finance), (3) financial and platform digitalization (fintech, Islamic crowdfunding), (4) technological innovation in business models (e.g., urban farming–aquaponics) that are value-framed, and (5) halal product/marketing innovation (halal assurance and halal trust). Behind that, the drivers are layered: individual values and psychology, Islamic HRM cultural orientation and organizational learning, Islamic finance architecture and regulation, and access to digital literacy and trust in the platform. The impact is multidimensional performance, access to ethical capital, halal market behavior, and social and religious environmental outcomes with strong contributions to SDG 8 and SDG 9, and footprints on SDGs 1–2, 3, 10, 11, 12, 13, 16, 17. This SLR offers an integrated financial innovation value framework and proposes SDGs micro-indicators; limitations mainly in the variation of measurements and the dominance of cross-section designs.

This study aims to develop an Indonesian traffic sign detection system using a transfer learning approach to improve road safety and traffic efficiency. The dataset was obtained from Kaggle and consists of 2,100 images across 21 traffic sign classes. The research stages include data collection, preprocessing to reduce noise and normalize image brightness, object detection using YOLOv5, and classification based on transfer learning with ResNet, VGG-16, and MobileNet architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the YOLOv5 model is capable of detecting traffic sign objects; however, the classification performance remains relatively low, with a mean Average Precision (mAP) value of 0.17. These findings suggest that further optimization is required in data preprocessing, dataset quality, and model parameter tuning to achieve better performance. This study demonstrates that transfer learning has significant potential for developing computer vision-based traffic sign detection systems, although further improvements are necessary to ensure robustness under real-world Indonesian traffic conditions.

The rapid development of information technology has provided significant opportunities to improve the efficiency of academic administration in schools. One of the common problems faced by educational institutions is the manual process of subject scheduling, which is time-consuming and prone to schedule conflicts among teachers, classes, and learning time. This problem is also experienced by SMPK Flos Carmeli, where the preparation of subject schedules has not yet been supported by an integrated computerized system. This study aims to design and develop a web-based subject scheduling application at SMPK Flos Carmeli using the Model–View–Controller (MVC) architecture. The research method employed is Research and Development (R&D) with the Waterfall software development model, which includes the stages of requirements analysis, system design, and application implementation. Data were collected through interviews, observations, and literature review to obtain system requirements that align with the school’s conditions. The application was developed using native PHP with the implementation of the MVC pattern to produce a structured, maintainable, and flexible system. The results show that the developed application is able to support the subject scheduling process in a faster, more accurate, and well-organized manner. The system provides features for managing teacher data, class data, time slots, schedule arrangement, and schedule printing, thereby minimizing schedule conflicts and improving the efficiency of school administrative work. Therefore, this subject scheduling application is expected to support the digitalization of academic administration and enhance the effectiveness of the teaching and learning process at SMPK Flos Carmeli.

The hyper-connected work era has fundamentally blurred the temporal and psychological boundaries between professional and personal spheres, triggering a productivity paradox phenomenon where increased digital connectivity is inversely proportional to long-term cognitive efficiency. Consequently, this research aims to explore deeply and comprehensively the critical roles of cognitive resilience and digital wellbeing strategies as vital preventive mechanisms against the rising risk of burnout resulting from cumulative cognitive fatigue. Employing a qualitative approach with a rigorous literature review design, this study synthesizes extensive secondary literature retrieved from accredited academic databases and global policy reports within the last five years to analyze current trends and theoretical frameworks. The in-depth analysis reveals that cognitive resilience, significantly bolstered by metacognitive digital literacy, functions as an adaptive shield against the mounting pressures of information overload and attention fragmentation. Furthermore, the findings emphatically confirm that sustainable productivity is not achieved through borderless connectivity, but rather necessitates disciplined cognitive energy management and the strict implementation of "right to disconnect" protocols. Concluding the study, it is strongly recommended that organizations integrate comprehensive digital wellbeing policies into their work culture architecture to safeguard long-term employee performance and ensure organizational sustainability amidst the challenges of the attention economy.