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Desa Motu is an area that experiences limited internet access due to the unavailability of conventional ISP services and weak cellular signal coverage. This study aims to develop a community-based RT/RW Net network system to provide internet access by utilizing Starlink as the main ISP and distributing connectivity through networking devices such as MikroTik routers, access points, switches, and fiber optic media. Network testing was conducted by measuring download and upload speeds using network testing ap-plications, evaluating connection stability through latency (ping) measurements, and assessing signal coverage at several user locations. The results show that the implemented RT/RW Net network is able to provide a stable internet connection with consistent speeds at different testing times, as well as optimal signal distribution across multiple measurement points. The authentication system using vouchers, PPPoE, hotspot login, and MAC Binding functions properly, and the free educational access feature also operates effectively. These findings indicate that the RT/RW Net–based community network model can serve as an affordable and sustainable solution for expanding internet access in rural areas.

Intellectual property rights (IPR) are an important legal tool for trademark ownership, protecting business quality, and protecting a company's economic interests. Consumers build trust in trademarks because they signal distinct product quality and reflect a positive and consistent corporate image. Trademark protection is regulated by national law under Trademark Law No. 20 of 2016, which provides legal certainty for trademark owners. This law stipulates that a trademark is only valid if it has distinctive elements, is not imitative, and has been officially registered with an authorized institution. The "first come, first served" principle in Indonesian trademark law can be interpreted as a mechanism that grants rights to the first party to file a valid application. However, the application of this principle in practice often raises complex legal issues, particularly when a trademark has already gained widespread public recognition prior to its formal registration. This study focuses on evaluating this legal protection through a normative legal research method by examining applicable laws, regulations, and court decisions related to trademark disputes in Indonesia. The Geprek Bensu dispute has attracted public attention because it highlights the conflict between legal provisions regarding trademark ownership and public perception. This case demonstrates that the existing legal framework still requires further refinement to balance the interests of trademark registrants with those of parties who have built public reputation through prior commercial use. Therefore, legal reform and consistent law enforcement are essential to ensure fair and comprehensive trademark protection in Indonesia.

This study was conducted to evaluate the impact of financial performance, capital structure, and good corporate governance on entities. The approach used is quantitative with a causal associative method. The research observations utilize secondary data sourced from the financial statements of entities listed on the stock exchange during the 2020–2023 period. The research sample was determined using a purposive sampling technique based on predefined criteria, totaling 160 observations. The analytical method employed is multiple linear regression, preceded by classical assumption tests. The results reveal that financial performance and good corporate governance have a positive and significant effect on the quality of financial statements, while capital structure has a significant negative effect. Simultaneously, the three independent variables are proven to significantly affect the quality of financial statements, with a coefficient of determination of 68%. These findings support agency theory and signaling theory in explaining the financial reporting behavior of entities. The implications of this study indicate that improving financial performance and implementing good corporate governance can enhance the quality of financial statements. Furthermore, optimal management of capital structure is also necessary to reduce the risk of financial statement manipulation.

This study analyzes the effect of capital structure and firm growth on firm value with dividend policy as a mediating variable in property and real estate companies listed on the Indonesia Stock Exchange for the period 2019-2024. The volatility of the property sector influenced by global and domestic economic conditions encourages the importance of understanding firm value formation mechanisms. The research method uses a quantitative approach with purposive sampling technique resulting in 66 observations from 11 companies during the research period. Data analysis uses Partial Least Squares-Structural Equation Modeling through SmartPLS application to test relationships between variables. The results show that capital structure and firm growth have no direct significant effect on firm value, but have a significant negative effect on dividend policy. Dividend policy has a significant positive effect on firm value and is able to fully mediate the effect of capital structure and firm growth on firm value with Variance Accounted For values of 151.6% and 90.4% respectively. These findings confirm the importance of dividend policy as a credible signaling mechanism regarding the company's ability to generate sustainable cash flows in creating value for shareholders amid the volatility of Indonesia's property sector.  

This study examines the influence of earnings quality and dividend policy on firm value at PT Alkindo Naratama Tbk during the period 2014–2024. Firm value is measured using the Price to Book Value (PBV), earnings quality is proxied by the ratio of operating cash flow to net income (QOE), and dividend policy is measured using the Dividend Payout Ratio (DPR). This research adopts a quantitative approach with an associative causal design using secondary data obtained from the company’s quarterly financial reports over eleven years, resulting in 44 observations. The analysis method applied is multiple linear regression. The findings reveal that earnings quality has a positive and significant impact on firm value. Dividend policy also shows a positive and significant effect on firm value. Simultaneously, earnings quality and dividend policy significantly influence firm value. The coefficient of determination indicates that a large proportion of firm value variation can be explained by these two variables. These results support signaling theory, which suggests that high earnings quality and stable dividend distribution provide positive signals to investors and increase market confidence in the company. The study contributes to financial management literature by highlighting the importance of financial performance indicators in determining firm value.

Background: In the competitive healthcare landscape, where human resources are pivotal to organizational success, affective commitment defined as emotional attachment to the workplace is essential for nurse retention and service quality. Satya Negara Hospital in North Jakarta exemplifies the challenges faced by Indonesian healthcare institutions, with a notably high nurse turnover intention of 30% in 2024, signaling low affective commitment and underscoring the urgency to identify its drivers. While person-job fit and career development are established antecedents of commitment, their combined influence within Indonesia’s nursing context, along with the potential moderating role of meritocracy, remains underexplored.. Methods:  Using a quantitative, cross sectional design, data were collected via a validated questionnaire from all 108 nurses at the hospital and analyzed using multiple regression.. Results: The results revealed that person-job fit, career development, and meritocracy collectively explain 96.6% of the variance in affective commitment. Furthermore, when meritocracy was tested as a moderating variable, the explanatory power of the model increased significantly, with the adjusted R² value rising from 96.6% to 98.5%. This indicates that the presence of a meritocratic system substantially amplifies the positive effects of both person-job fit and career development on commitment.. Conclusion: The study concludes that a synergistic combination of job fit, growth opportunities, and a merit-based system is fundamental to fostering nurses’ emotional attachment. These findings contribute to organizational and psychological theory integration and offer practical human resource strategies for enhancing nurse commitment and reducing turnover in healthcare settings.

Digital images often experience noise disturbances that can reduce visual quality and interfere with the image analysis process. One common type of noise is salt and pepper noise, especially in grayscale images, which is characterized by the random appearance of black and white dots. This study applied the Deep Convolutional Autoencoder (DCAE) method with a skip connection mechanism to eliminate salt and pepper noise in grayscale images measuring 256×256 pixels. The dataset used consists of 300 pairs of clean images and noisy images that have gone through the preprocessing stage, including normalization and data augmentation. The model was trained using an Adam optimizer with a Mean Squared Error (MSE) loss function and validated through a train-test split scheme to avoid overfitting. Model performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The test results showed that the DCAE model with skip connections was able to effectively reduce noise while maintaining the main structure of the image based on the PSNR and SSIM values obtained, and showed better performance than conventional median filters. In addition, the model was successfully implemented into a Streamlit-based application to perform the image denoising process interactively, making it easier for users to experiment and visualize results in real-time.

Company value is an important indicator that reflects company performance and investor perceptions of future business prospects and sustainability. Various strategic decisions made by management, such as capital intensity management, investment decisions, and tax aggressiveness policies, play a significant role in shaping company value. This study aims to examine and analyze the effect of capital intensity, investment decisions, and tax aggressiveness on company value through a literature review approach. The method used is a literature review by examining various relevant national and international scientific articles obtained from academic databases such as Google Scholar, Publish or Perish, and SINTA. The results of the study show that capital intensity has a positive effect on company value because it reflects long-term production capacity and operational efficiency. Investment decisions have also been proven to have a positive effect on company value because they signal management's optimism about future growth prospects. Meanwhile, tax aggressiveness can increase company value through tax savings and increased cash flow, but it has the potential to cause reputational and governance risks if done excessively. Overall, the reviewed literature shows that these three variables have an impact on company value, with the caveat that optimal and transparent management is necessary. This study is expected to serve as a reference for further research and as a consideration for company management and investors in making strategic decisions.

This study investigates the determinants of dividend payout policy in energy sector firms listed on the Indonesia Stock Exchange during the 2020–2024 period. Dividend policy is a critical issue in emerging markets, especially in capital-intensive industries with high investment needs and earnings volatility. The research examines whether profitability and ownership structure—specifically institutional and managerial ownership—significantly influence dividend payout decisions, considering firm characteristics. The study analyzes the effect of profitability, institutional ownership, and managerial ownership on the dividend payout ratio, while controlling for firm size and leverage. A quantitative approach is used, employing pooled ordinary least squares (OLS) regression on 245 firm-year observations. Dividend payout ratio is measured as dividend per share divided by earnings per share, profitability is proxied by return on equity, and ownership variables are expressed as shareholding proportions. Descriptive analysis and classical assumption tests precede hypothesis testing. The results show that profitability positively and significantly affects dividend payout, suggesting that firms with better financial performance tend to distribute higher dividends. Firm size also positively influences dividend policy, while leverage negatively impacts it, reflecting the role of financial capacity and capital structure. However, institutional and managerial ownership do not show significant effects on dividend payout decisions. The findings indicate that dividend policy in Indonesian energy firms is primarily driven by financial performance and structural characteristics rather than ownership-based governance mechanisms. This study offers sector-specific evidence that refines agency and signaling perspectives on dividend policy in emerging markets, with practical implications for managers, investors, and regulators.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

This study aims to analyze the reaction of the Indonesian capital market to the announcement of Donald Trump’s import tariff policy using an event study approach. Market reactions are measured through abnormal return and trading volume activity of exporting companies listed on the Indonesia Stock Exchange (IDX), with an event window of three trading days before and three trading days after the initial tariff announcement on April 2, 2025 and the revised tariff announcement on July 15, 2025. This study employs secondary data in the form of daily stock prices and trading volumes, analyzed using descriptive statistics, normality tests, and the Wilcoxon Signed Rank Test. The results indicate that the Indonesian capital market reacts to the announcement of Donald Trump’s import tariff policy, as reflected by differences in abnormal return and trading volume activity before and after the announcements, thereby supporting signaling theory and the semi-strong form of market efficiency.

Choking is a dangerous thing for children, especially children with special needs who require proper handling, and parents as the closest people to the child must have first aid skills for choking. The purpose of this study is to determine the effect of ANSIVI (Animation and Simulation Video) Choking Management on parents' ability in first aid for choking. The research design was a pre-experimental design with a one-group pretest-posttest approach without control. The purposive sampling technique obtained 36 respondents. The research instrument used an observation sheet for first aid for choking. The statistical test was the Wilcoxon Signed Rank Test with α 0.05. The results of the study before being given the ANSIVI (Animation and Simulation Video) Choking Management intervention showed that all (100%) respondents had insufficient ability, and after the intervention the majority (52.8%) of respondents had sufficient ability. The results of the statistical analysis obtained a p-value of 0.013 < α 0.05, so there is an effect of ANSIVI (Animation and Simulation Video) choking management on parents' ability in first aid for choking. The ANSIVI method (Video Animation and Simulation) combines two methods so that it is more interesting, interactive, and effective because the video is captured by the eyes and ears and then will be detected and converted into signals to the optic nerve and auditory nerve and forwarded to the brain, program area, and frontal area to be associated so that it will affect the respondent's ability. The ANSIVI method can be used in education for all parents and school residents as a first aid effort for choking, especially for children with special needs.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

The increasing demand for low latency and high-throughput multimedia applications has spurred significant advancements in hardware software co design. This study explores the integration of custom digital signal processing (DSP) hardware accelerators with optimized software frameworks to enhance deep learning accelerated DSP tasks. The proposed co design approach significantly reduces latency and improves throughput compared to traditional software-only DSP implementations. Through the development of custom hardware accelerators built with FPGA technology, the system achieves up to a 1.85x reduction in latency and a 1.5x improvement in throughput for real-time multimedia tasks such as image recognition, video decoding, and audio processing. The combination of hardware and software optimizations allows for better resource utilization, enabling the parallel processing of computationally intensive tasks while the software framework handles less demanding operations. Additionally, the co design system demonstrated improved energy efficiency, making it highly suitable for embedded systems. The results show that the hardware software co design approach offers substantial advantages in performance, latency reduction, and energy efficiency, positioning it as a viable solution for real-time multimedia applications. The findings have important implications for applications requiring fast data processing, such as autonomous driving, healthcare, and disaster management. Future research could explore alternative hardware accelerators, advanced software optimizations, and AI-based resource management to further improve the system’s efficiency and scalability for more complex multimedia tasks.

This study investigates the integration of high performance communication protocols with adaptive signal processing engines in multi-core systems, aiming to enhance scalability, throughput, and inter-core communication efficiency. The challenges inherent in traditional multi core architectures, such as communication overhead, latency, and scalability limitations, are addressed through the incorporation of Network-on-Chip (NoC) architectures and adaptive signal processing techniques. By using a multi-core digital signal processing (DSP) platform, the study evaluates the performance improvements achieved by this integration under varying workloads and core configurations. The experimental results show a 35% improvement in throughput and a 25% reduction in communication latency, highlighting the effectiveness of adaptive communication protocols in managing data traffic between cores and reducing bottlenecks. The integration of NoC architecture facilitates parallel data transfers, while adaptive signal processing engines ensure that data flows more efficiently across the cores, enhancing system responsiveness, especially under high data rate conditions. Furthermore, the study explores the scalability of the proposed system, demonstrating its ability to maintain high performance as core counts increase. The findings emphasize the potential of combining advanced communication protocols with adaptive signal processing for optimizing multi-core system performance. Practical implications of this research include the design of scalable, flexible, and efficient multi core architectures suitable for complex, data-intensive applications. Future research should focus on further refining communication protocols and exploring additional integration strategies to enhance the adaptability and scalability of multi-core systems in next-generation computing environments.

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

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.

Contemporary defense and security challenges now come from both non-military threats (such as cyber attacks, disinformation campaigns, and economic pressure) and military threats (traditional armed force). These often operate in the gray zone, meaning actions are taken below the threshold of armed conflict as defined under international law. This situation signals the rise of modern armed conflict, which is growing more intense and causing a crisis in International Humanitarian Law (IHL) enforcement. This study aims to describe modern armed conflict and its challenges to IHL. The research used a normative juridical approach and analyzed statutes. Legal materials reviewed included various international legal instruments, which were examined qualitatively and normatively. The results show that modern armed conflict challenges International Humanitarian Law with non-linear conflicts (conflicts with unclear frontlines or participants), proxy actors (groups acting on behalf of states), and cyber threats or propaganda. Therefore, IHL should be updated to include more comprehensive regulations

Video steganography faces fundamental challenges in balancing embedding capacity, imperceptibility, and robustness, where conventional Least Significant Bit (LSB) methods often produce visual artifacts such as flickering. To address this, this research proposes an advanced method named Adaptive Multi-layer LSB, which dynamically adjusts the number of embedded bits in each pixel based on a multi-factor analysis of the video's spatial and temporal characteristics. This adaptation mechanism is evaluated through three primary criteria: brightness level, local texture complexity, and inter-frame motion stability. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Frame Difference Stability Index (FDSI) metrics demonstrates that the proposed method achieves high visual quality, with an average PSNR of 42.15 dB and SSIM of 0.985. These results significantly outperform the non-adaptive approach, which only recorded a PSNR of 38.5 dB. More importantly, the FDSI value of this method (1.25) is much lower compared to the non-adaptive approach (3.40), demonstrating its superiority in maintaining temporal stability. Thus, this approach provides a significant contribution to enhancing security and quality in video steganography practices. Abstract: Video steganography faces fundamental challenges in balancing embedding capacity, imperceptibility, and robustness, where conventional Least Significant Bit (LSB) methods often produce visual artifacts such as flickering. To address this, this research proposes an advanced method named Adaptive Multi-layer LSB, which dynamically adjusts the number of embedded bits in each pixel based on a multi-factor analysis of the video's spatial and temporal characteristics. This adaptation mechanism is evaluated through three primary criteria: brightness level, local texture complexity, and inter-frame motion stability. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Frame Difference Stability Index (FDSI) metrics demonstrates that the proposed method achieves high visual quality, with an average PSNR of 42.15 dB and SSIM of 0.985. These results significantly outperform the non-adaptive approach, which only recorded a PSNR of 38.5 dB. More importantly, the FDSI value of this method (1.25) is much lower compared to the non-adaptive approach (3.40), demonstrating its superiority in maintaining temporal stability. Thus, this approach provides a significant contribution to enhancing security and quality in video steganography practices.