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Corporate financial performance is an important factor in maintaining business sustainability amid increasingly intense competition. One of the commonly used indicators of financial performance is Return on Assets (ROA), which reflects a company’s ability to generate profits through the efficient use of its assets. Corporate profitability is influenced by various internal factors, including capital structure and liquidity. This study aims to analyze the effect of Debt to Equity Ratio (DER) and Current Ratio (CR) on Return on Assets (ROA). This research employs a quantitative approach using secondary data obtained from corporate financial statements. The research sample consists of 36 observations selected through purposive sampling. Data analysis techniques include descriptive statistical analysis and multiple linear regression analysis using SPSS software. The results show that, partially, the Debt to Equity Ratio does not have a significant effect on Return on Assets, while the Current Ratio has a positive and significant effect on Return on Assets. Simultaneously, Debt to Equity Ratio and Current Ratio have a significant effect on Return on Assets, with Current Ratio being the most dominant variable. The findings indicate that effective liquidity management plays a crucial role in improving corporate profitability. The implications of this study are expected to provide useful insights for corporate management in making financial decisions, particularly related to liquidity management and capital structure.

Model driven Development (MDD) has emerged as an efficient software engineering methodology that focuses on using high-level models as primary artifacts throughout the software development process. The methodology involves transforming abstract models into detailed designs, and eventually into executable code, with the assistance of automated tools. This study evaluates the impact of MDD on the Verification and Validation (V&V) processes within secure enterprise software systems. By comparing MDD-based projects with traditional code-centric development approaches, the study highlights the advantages of MDD in reducing verification time, minimizing defect leakage, and improving the traceability of security requirements. MDD significantly enhances V&V efficiency by automating key processes, which allows for earlier error detection and better resource utilization. Additionally, MDD strengthens security compliance by integrating security requirements early in the development lifecycle, ensuring better alignment between system requirements and their implementation. Despite the clear benefits, challenges such as the lack of standardized tools and the need for specialized expertise in model development were also encountered during the study. The findings of this research offer important insights for enterprise software development teams looking to adopt MDD for more efficient and secure V&V processes. Future research should focus on the long-term impact of MDD on security compliance, as well as its adoption across different industries, to fully understand the practical benefits and challenges of implementing MDD in diverse real-world environments.

PT Adhi Karya (Persero) Tbk is a State-Owned Enterprise (BUMN) in Indonesia that operates in the construction industry. PT. Adhi Karya (Persero) Tbk. was awarded the contract for the Yogyakarta-Bawen toll road construction project, Package 1, Section 1. Construction safety is a very important aspect in the project. The toll road construction project involves work at heights, especially on erection grider work. Erection grider work is a high-risk job, so construction safety must be guaranteed. If construction safety is not guaranteed in the work environment, workers will be more careful in working to reduce risks so that work slows down. This can reduce work productivity. However, when safety is guaranteed, workers will feel safe to work so that it can increase work productivity. This study aims to determine the effect of construction safety (X) on work productivity (Y). The data collection method was carried out by distributing questionnaires to 30 respondents of erection grider workers. Data processing used the Structural Equation Modeling–Partial Least Squares (SEM PLS) method with SmartPLS 4.0 software. The results of data processing showed that construction safety had a significant influence on work productivity with a t-statistic value of 7.329 and a p-value of 0.000.  

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world 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.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

Derivatives of substituted oxazol-5-one (1–3) and substituted oxazolo-isoxazole (4–6) were successfully synthesized through a multi-step reaction process. Initially, glycine was reacted with acetic anhydride to produce acetyl glycine as a key intermediate. This intermediate subsequently underwent a condensation reaction with various substituted benzaldehydes, leading to the formation of oxazol-5-one derivatives (1–3). In the final synthetic step, these oxazol-5-one derivatives were reacted with hydroxylamine hydrochloride to yield the corresponding oxazolo-isoxazole compounds (4–6). The chemical structures of all synthesized compounds were characterized and confirmed using Fourier Transform Infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (¹H NMR) spectroscopy. Furthermore, molecular docking studies were carried out to evaluate the potential biological interactions of the prepared compounds. Docking simulations were performed using PyRx software, while visualization and interaction analysis were conducted employing PyMOL and Discovery Studio. The combined experimental and computational approaches provide valuable insights into the structural properties and potential biological relevance of the synthesized compounds.

Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.

This study aims to analyze the effect of price as a component of the marketing mix of NutriSari products on consumers’ purchase intention at the STIE YPBI Campus Canteen. Price is a crucial element of the marketing mix that directly influences consumer perceptions and purchasing decisions. Therefore, understanding consumers’ price perceptions is essential for companies in developing effective marketing strategies and maintaining competitiveness. This research employed a quantitative descriptive approach, with data collected through questionnaires distributed to consumers at the STIE YPBI Campus Canteen. The population of this study consisted of 40 active consumers within the campus canteen environment. Data were analyzed using validity and reliability tests, normality testing, simple linear regression analysis, t-test, and F-test with the assistance of SPSS software. The results indicate that price has a significant effect on purchase intention, as evidenced by an F-test value of 5.440 and a coefficient of determination of 54.40%. This finding implies that 54.40% of consumers’ purchase intention is influenced by price, while the remaining percentage is affected by other factors not examined in this study. Furthermore, the t-test results show that the calculated t-value (2.332) is greater than the critical t-value (2.024), indicating a positive and significant effect of price on purchase intention. The indicators of price affordability, price-quality suitability, price competitiveness, and price-benefit suitability collectively contribute to increasing consumers’ purchase intention at the STIE YPBI Campus Canteen.

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.

This research aims to examine the influence of store atmosphere, brand awareness, and lifestyle on purchasing decisions at Kobapa Store in Delitua District. This study uses a quantitative research method with a questionnaire research design. The subject of this research is the products of Kobapa Store, and the object of the study is consumers who purchase products at Kobapa Store, totaling 97 respondents. Data collection techniques include observation, questionnaires, and documentation. Data analysis techniques involve validity tests, reliability tests, and classical assumption tests. The data analysis method used is multiple linear regression with the assistance of SPSS 25 software. The t-test results show that store atmosphere has a positive effect on purchasing decisions. Brand awareness has a positive effect on purchasing decisions. Lifestyle also influences purchasing decisions. The F-test results indicate that store atmosphere, brand awareness, and lifestyle simultaneously affect purchasing decisions at Kobapa Store in Delitua District, with an F calculated value of 4.141 > F table value of 2.700, and a significance value of 0.008 < 0.05. The adjusted coefficient of determination (R Square) is 0.338, meaning that 33.8% of the dependent variable can be explained by the independent variables, while the remaining 66.2% is explained by other variables outside the ones studied.

This study aims to analyze the effect of Current Ratio (CR), Debt to Asset Ratio (DAR), and Inventory Turnover on Earning Per Share (EPS). This research employs a quantitative method with a causal-comparative ex-post facto approach. The population includes food and beverage companies listed on the Indonesia Stock Exchange (IDX) for the 2020-2023 period. The sampling technique used purposive sampling, resulting in 10 companies with a total of 40 observations. Data analysis was conducted using multiple linear regression utilizing SPSS version 25 software. The results indicate that partially, CR, DAR, and Inventory Turnover each have a significant effect on EPS. Simultaneously, these three independent variables significantly affect EPS with a determination coefficient of 83.7%. The implications of this study emphasize the importance of liquidity management, solvency, and inventory efficiency in improving corporate share profitability.

Sexually transmitted infections (STIs) are a reproductive health problem that is still common among adolescents and can have serious impacts if not handled properly. Low levels of knowledge and unfavorable attitudes towards STIs have the potential to increase risky behavior among students. Objectives. This study aims to determine the level of knowledge and attitudes of students towards sexually transmitted infections at MAS Darul Mursyid. Methods. This study employed a cross-sectional methodology and a quantitative descriptive design. All MAS Darul Mursyid students were included in the study population, and a total sampling procedure was used to pick 149 respondents. A questionnaire with 20 statements about knowledge and 10 statements about attitudes around STIs was used to collect data. The SPSS software was used to analyze the data univariately, and the results were displayed as percentages and frequency distributions. Results and Discussion. According to the findings, 78 respondents (52.3%) had an adequate level of understanding, followed by 63 respondents (42.3%) in the poor category and 8 respondents (5.4%) in the good category. In the meanwhile, most students had a favorable opinion of STI prevention. Conclusion: Even though the majority of respondents expressed support for STI prevention initiatives, additional health education and instruction are still required to give students a more thorough grasp of STI prevention.

The implementation of the zoning-based admission policy (PPDB) in Semarang City continues to face challenges related to the accuracy of distance measurement and the transparency of information provided to the public. This study aims to examine the application of Geographic Information Systems (GIS) in defining zoning boundaries for public junior high schools in Semarang City and integrating the results into a web-based information platform. A quantitative descriptive approach was employed, incorporating spatial analysis through a 3-kilometer buffer radius using QGIS software. The results indicate that buffer analysis is effective in delineating priority domicile zones based on school coordinate data. These findings are integrated into a GIS-based website that presents visual information on school locations, enrollment capacity, and final score calculation mechanisms in accordance with current regulations. The proposed system contributes to improving information transparency, enabling the public to better understand admission opportunities while supporting government decision-making in promoting equitable access to education. For future development, the use of road network analysis is recommended to obtain more realistic distance estimations.

Brebes Regency is one of the main national centers of shallot production in Indonesia, where agricultural land dynamics play a crucial role in sustaining production performance. Rapid infrastructure development and land-use change have raised concerns regarding the sustainability of shallot cultivation areas. This study aims to analyze spatial–temporal changes in shallot cultivated area and production in Brebes Regency during 2023–2024 and to examine the relationship between land area changes and production levels at the sub-district scale. A descriptive quantitative approach based on spatial analysis was employed using Geographic Information Systems (GIS). Spatial data consisted of administrative boundary maps, while non-spatial data included shallot cultivated area and production data obtained from the Brebes Regency Office of Agriculture and Food Security. Data integration and analysis were conducted using QGIS through attribute joining and inter-annual comparison. The results indicate that total shallot cultivated area increased from 26,331 ha in 2023 to 28,628 ha in 2024, accompanied by a significant rise in production from 289,942.05 tons to 409,106.90 tons. Spatial analysis reveals that major production centers remain concentrated in the central and northern sub-districts, particularly Wanasari, Larangan, Bulakamba, and Tanjung. Although a positive relationship between land expansion and production increase is evident, variations among sub-districts suggest that productivity and local conditions also play important roles. The findings highlight the effectiveness of GIS-based analysis in supporting spatially informed agricultural land management and policy formulation.

The increasing demand for infrastructure necessitates the construction of reliable bridges with long service lives to ensure smooth mobility. This research focuses on the Griya Lingga Permai Bridge over the Asam River in Jambi City, which is scheduled for widening as part of the Asam River Revitalization program aimed at flood mitigation. Although the project faces delays due to land acquisition constraints, this postponement is leveraged as a strategic opportunity to conduct a design review of the planned bridge superstructure.The bridge is designed using a reinforced concrete "T" beam girder structure. This study aims to perform a design review of the planned superstructure. This review includes the verification of calculations, structural dimensions, and a comparison of reinforcement calculation results, which are crucial for ensuring the future safety and reliability of the bridge against design loads. To support the validation and analysis process, the research utilizes STAAD.Pro software to obtain more accurate and detailed data regarding structural capacity. The results of this study will provide solid technical recommendations for the continuation of the construction project.

The rapid growth of information technology has encouraged small and medium-sized food businesses to adopt digital systems in order to improve service quality and operational efficiency. Ayam Goreng Pakdhe, a local culinary business, still relies on manual ordering processes that often lead to order inaccuracies, slow service, and limited access to sales data. This study aims to design and develop a web-based ordering system for Ayam Goreng Pakdhe using the Spiral software development method, which emphasizes iterative development, risk analysis, and continuous user feedback. The research method consists of requirement analysis, system design, implementation, and evaluation carried out in repeated cycles according to the Spiral model. Data were collected through observation, interviews with business owners and employees, and analysis of existing business processes. The results show that the proposed web-based system is able to streamline the ordering process, reduce human errors, and provide real-time information on orders and sales reports. Furthermore, the iterative nature of the Spiral method allows the system to be adjusted according to user needs and potential risks identified during development. The implementation of this system is expected to enhance operational efficiency, support better decision-making, and increase customer satisfaction, thereby contributing to the digital transformation of small-scale culinary enterprises.

This study aims to analyze how the integration of traditional knowledge and digital knowledge shapes organizational strategies in Micro, Small, and Medium Enterprises (MSMEs) in responding to an increasingly competitive business environment. A quantitative approach was employed by applying variance-based Structural Equation Modeling (SEM) using SmartPLS 3 software to 100 MSME respondents. The research model was developed by incorporating three main latent constructs, namely digital knowledge, traditional knowledge, and organizational strategy, which were measured using several indicators relevant to the MSME context. The analysis results indicate that all research instruments meet good reliability and validity criteria, with outer loading values ranging from 0.719 to 0.902, Average Variance Extracted (AVE) values above 0.64, and composite reliability exceeding 0.87. Structural model testing reveals that digital knowledge has a strong and significant effect on organizational strategy (β = 0.799), while traditional knowledge shows a very small effect (β = 0.024). The research model explains 66.4% of the variance in organizational strategy. These findings confirm that digital transformation is the primary factor in shaping competitive strategies among MSMEs, while traditional knowledge continues to serve as a foundation for local context and cultural values. Therefore, the implementation of a hybrid management approach is considered effective in assisting MSMEs in formulating more adaptive, innovative, and sustainable strategies.

This study examines the impact of financial performance indicators—activity, solvency, and liquidity ratios—on profit growth in pulp and paper manufacturing companies listed on the Indonesian Stock Exchange from 2019 to 2024. The research focuses on how variations in Total Assets Turnover, Inventory Turnover, Fixed Assets Turnover, Debt to Equity Ratio, and Quick Ratio affect profitability, especially during periods of economic instability like the COVID-19 pandemic. The aim is to identify which financial ratios have the most significant influence on profit performance. A quantitative research method was employed, utilizing secondary data from 42 observations of seven manufacturing firms, selected through purposive sampling. Multiple linear regression analysis, supported by SPSS software, was used to test the hypotheses. The findings show that all five ratios collectively have a significant impact on profit variations, with an F-statistic of 2.568 and a significance value of 0.044. However, when tested individually, only Total Assets Turnover and Inventory Turnover showed significant effects, while Fixed Assets Turnover, Debt to Equity Ratio, and Quick Ratio did not. The coefficient of determination (R²) was 0.263, indicating that 26.3% of the variation in profit can be explained by the analyzed variables.

Abstract. The development of digital media, especially YouTube, has made this platform not only a means of entertainment, but also a source of information, education, and self-reflection for students. Viewer satisfaction in consuming YouTube content is influenced by the quality of content presented by creators. This study aims to evaluate the impact of the quality of Nadia Omara's YouTube channel content on viewer satisfaction among students at Muhammadiyah University Kotabumi. This study applies a quantitative approach through a survey method. Data were collected through a closed questionnaire with a Likert scale distributed to 98 respondents selected using purposive sampling. The independent variable in this study is content quality, while the dependent variable is viewer satisfaction. Data analysis was conducted through validity testing, reliability testing, linearity testing, and simple linear regression analysis. Hypothesis testing was performed using the t-test (partial test) with the help of SPSS version 26 software. The results showed that the quality of Nadia Omara's YouTube channel content had a positive and significant effect on viewer satisfaction. This is evidenced by a regression coefficient value of 0.865 and a t-value of 11.937 with a significance level of 0.000 (< 0.05). These findings support the Uses and Gratifications Theory and Expectation Confirmation Theory, which emphasize the active role of the audience in evaluating media based on the fulfillment of needs and conformity with expectations empirically among college students.