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PT. XYZ faces administrative challenges in implementing the Contractor Safety Management System (CSMS), which has been managed manually from risk assessment and pre-qualification to contractor selection resulting in validation delays, document duplication, and limited monitoring. This study aims to: (1) identify the constraints of the manual CSMS at PT. XYZ; (2) design an electronic-CSMS (e-CSMS) application tailored to internal user needs; (3) analyze the contribution of e-CSMS to document management effectiveness; and (4) evaluate the design's alignment with the Occupational Health and Safety Management System (SMK3) principles as per Government Regulation No. 50 of 2012. The method employed is Research and Development (preliminary stage) using a descriptive qualitative approach through interviews, observations, and document studies. The design results include: (i) a unified digital form integrating procurement data input, risk assessment entries (severity and probability), and document upload and assessment for pre-qualification/selection in a single workflow; (ii) a monitoring dashboard with filtering and export functions; and (iii) internal access settings within PT. XYZ's HSSE portal. Functionally, this design addresses the main bottlenecks of the manual process by creating a more concise, structured, and documented verification workflow, thereby making document management more efficient and traceable. In terms of compliance, e-CSMS supports the implementation of SMK3 elements in the administrative domain of policy establishment, planning, and execution of OHS plans through consistent recording and verification mechanisms. Development recommendations include expansion to field implementation phases, automation of risk-level calculations, and an option for preliminary data entry by contractors with final verification remaining with the internal team.

The Checksheet process at PT. Automotive component companies are an important activity to maintain quality consistency in the production of automotive components, especially brackets. However, the inspection method that is still carried out manually causes waste of time, high use of paper, and increased operational costs. This study aims to reduce the cycle time of Patrol Check A-T-A, implement a digital (paperless) recording system, and improve process efficiency through the PDCA (Plan-Do-Check-Act) approach, Fishbone Diagram, and Why-Why Analysis. The results of the identification of the root of the problem show that the absence of a standardized digital system and the lack of management initiatives are the main causes of the long inspection process and high paper consumption. The implementation of E-Checksheet as a digital solution resulted in a significant reduction in cycle time, from 50 minutes to 10 minutes, as well as eliminating paper use by up to 100%. In addition, workflows become simpler, data is easier to monitor, and processes become more efficient. This study concludes that the digitization of Checksheet is able to increase operational effectiveness, reduce waste, and support continuous improvement in the manufacturing environment.

This study aims to analyze the effectiveness of the internal control system in the Personnel Division of PT Sendang Derma Pesona in supporting effective, efficient, and well-governed human resource management. The background of this research is based on the importance of internal control systems in preventing administrative errors, reducing the risk of fraud, and ensuring compliance with labor regulations, particularly in personnel and payroll functions. This study employs a descriptive qualitative approach using an operational audit method. Data were collected through questionnaires, interviews, and document observation covering recruitment procedures, personnel data management, employee transfers, authorization processes, as well as payroll and bank reconciliation procedures. The results indicate that, in general, the internal control system in the Personnel Division has been implemented adequately, as reflected by complete personnel documentation and multi-level authorization in decision-making processes. However, several weaknesses were identified, including the lack of proper segregation of duties between payroll preparation and salary payment, the absence of regular bank reconciliation, and the continued use of manual personnel archive management. These weaknesses indicate that the internal control system still needs to be strengthened to minimize the risk of errors and improve the efficiency of personnel administration. The findings of this study are expected to provide practical implications for management in improving internal control policies and procedures to support better corporate governance and sustainable organizational performance.

The digital era continues to evolve and bring innovation and change to various industries. Through digital transformation and strategies needed to overcome obstacles in the digital era, organizations can adapt and change. To achieve their goals, MSMEs must implement efficient marketing techniques in their operations. This community service method uses the PAR (Participatory Action Research) technique. This is in line with the conditions of the partner, Dwikora Village, which has human resources that are well known to both village officials and residents. The results found that due to a lack of understanding of digital marketing communication management procedures, they were unfamiliar with digital marketing techniques, different social media platforms, or how to measure their effectiveness. The Community Service Team conveyed their understanding of the importance of digital marketing communication in improving the knowledge and skills of residents as an effort to improve their marketing communication skills. This was because many residents, especially mothers, did not understand how to use digital marketing to promote their MSMEs. The team formed a content creation team or digital marketing communication managers, socialized the value of digital marketing communication to the sub-district residents in improving their knowledge and skills, and implemented a digital marketing communication message management strategy on Instagram. After these activities, partners can create interesting and relevant content that suits the interests of the target audience in Dwikora Village, namely its residents.

UMKM BPK Legend Batam is a Batak culinary business that serves Karo Roast Pork (BPK) as its main dish along with side dishes such as soup, saksang, and fried Indomie B2. In its operational activities, food product inventory management is still done manually and based on estimates, which can potentially lead to excess or shortages of ready-to-sell products. This situation can result in a decline in product quality, wasteful spending, and lost sales opportunities when demand increases. The objective of this community service activity is to design a more effective and efficient food product inventory control system through the application of the EOQ and ROP methods. The methods used include observation, interviews, and analysis of sales data and product availability per period. The results of the activity show that the application of EOQ is able to determine the optimal product inventory quantity, while ROP provides guidelines for the right time to replenish products. The product inventory control system that was designed helps BPK Legend MSMEs in reducing inventory costs, maintaining product availability, and supporting smooth service to customers.

Administration is an activity that must be managed effectively to support public service quality, and one of the main keys to achieving a good administrative system is the utilization of information technology, which is currently developing rapidly. Administrative activities at the Wendewa Barat village hall are still carried out using conventional and ineffective methods, particularly in population data recording, which relies on manual bookkeeping. This approach is time-consuming, labor-intensive, prone to errors, and incurs higher operational costs. Therefore, an integrated system is needed to manage administrative activities more efficiently, accurately, and systematically. This research aims to develop a web-based population administration system to improve administrative performance at the Wendewa Barat village office. The system development employs the waterfall method, which consists of several stages, including requirements analysis, system design, implementation, system testing, and maintenance. The system is built using the PHP programming language with the CodeIgniter framework and utilizes a MySQL database for data management. Additionally, jQuery is applied in several system functions to enhance system interactivity, effectiveness, and user interface attractiveness.

This study aims to analyze the need for barges and tugboats in distributing Crude Palm Oil (CPO) in the West Papua region. The analysis is conducted based on three main aspects: the balance of supply and demand, ship operational costs, and shipping time, to determine the most efficient mode of sea transportation. The research method used is a quantitative approach by processing CPO distribution data owned by PT Kurhanz Trans in 2024. The results show that the combination of a 240-foot barge with a 5,000 HP tugboat is the most optimal alternative. This combination is able to balance cargo capacity, operational cost efficiency, and shipping time. The total operating cost of a 240-foot barge is recorded as lower than a 230-foot barge, with the same shipping time of 70 hours for a distance of 140 nautical miles. Thus, the 240-foot barge can be recommended as the best alternative to support the smooth distribution of CPO in the West Papua region efficiently and economically.    

Developments in information technology have had a significant impact on business communication and negotiation processes in various industrial sectors. Technological advances such as email, video conferencing, instant messaging applications, cloud-based information systems, and other digital media have created new, faster, more efficient, and more documented communication models. This study uses a descriptive approach based on a literature review to analyze how information technology is transforming interaction patterns, strategies, and negotiation processes in the modern business environment. The results indicate that information technology plays a strategic role in increasing the effectiveness of business communication and accelerating the negotiation process. Advantages such as cost savings, time flexibility, global access, and automated documentation are the main reasons for the increasing use of digital media in the business world. However, digital negotiation also presents challenges such as the loss of non-verbal context, the risk of miscommunication, data security issues, and limitations in interpersonal relationships. The conclusion of this study confirms that successful negotiations in the digital era are greatly influenced by interpersonal communication skills, digital literacy, appropriate media selection, and the implementation of negotiation strategies that adapt to technological changes.

This research has a crucial objective to facilitate primary school (SD) teachers so they are able to effectively and efficiently integrate and present Social Studies (IPS) material enriched with the content of local wisdom. Local wisdom is defined as a treasure inherited through generations, encompassing in-depth knowledge, real practices, and fundamental values that have developed and been firmly held within the local community. This foundation makes the learning process not only informative but also highly relevant and meaningful for students’ lives. This article specifically highlights that the integration of values, culture, and knowledge sourced from the local area can produce significant positive impacts. These impacts include improving students' conceptual understanding of Social Studies material, fostering their active participation in the classroom, and honing essential critical thinking skills. Teachers are encouraged to use a contextual approach or a real-life-based approach, so the material taught feels close to the students' experiences and is easier to grasp. The values embedded in local wisdom will function as a strong bridge in understanding every material concept. Thus, the knowledge acquired by students does not only stop at the cognitive level (merely theoretical knowledge), but can also be implemented concretely in the form of real actions and behavior outside the school environment. This local wisdom-based learning approach will ultimately become an important connection that guides students to act appropriately, ethically, and solution-oriented in responding to various problems they face in their daily lives.

This study aims to design, implement, and compare the performance of the Backpropagation algorithm from Artificial Neural Networks and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in predicting the optimal daily rice requirement at Grillme Restaurant in Pontianak. The main problem faced by the restaurant is the uncertainty in determining the required daily rice stock, which periodically results in either understocking (shortage) or overstocking (wastage), leading to operational losses. To address this, the study utilizes historical daily rice sales data from January 2023 to April 2025 as the database for training and testing both predictive models. The SARIMA approach is employed to capture time series components (trend and seasonality), while Backpropagation is utilized to model non-linear patterns. Comparative test results indicate that the SARIMA model achieved superior accuracy compared to the Backpropagation model. This is confirmed by the Mean Absolute Percentage Error (MAPE) value of the SARIMA algorithm being 17.35%, which is lower than the MAPE value of Backpropagation at 19.62%. The MAPE values obtained by both models demonstrate good predictive capability, but it is concluded that SARIMA is more recommended for a more efficient and planned management of rice stock at Grillme Restaurant in Pontianak.

Employee performance is a key factor in determining organizational success in achieving its strategic objectives. Various management studies indicate that employee performance is not solely influenced by individual factors, but is also significantly affected by organizational factors, particularly organizational planning and organizational structure. Effective organizational planning provides a clear direction and strategic framework that guides employees in performing their tasks, while an appropriate organizational structure facilitates coordination, clarifies authority, and supports the efficient execution of work. This study aims to analyze the influence of organizational planning and organizational structure on employee performance through a systematic literature review approach. The research method employed is a systematic literature review by examining relevant national and international journal articles published within the last ten years. Data were collected from reputable academic databases to ensure the credibility and relevance of the sources. The selected studies were analyzed qualitatively to identify patterns, relationships, and key findings related to the research variables. The results of the literature review indicate that systematic and well-formulated organizational planning contributes positively to improving goal clarity, work coordination, and employee motivation. Furthermore, an organizational structure that is aligned with organizational strategies plays an important role in clarifying task distribution, authority lines, and overall work effectiveness. The findings also reveal that organizational planning and organizational structure are interrelated and mutually reinforcing in shaping employee performance. This study concludes that the integration of organizational planning and organizational structure is a crucial factor in enhancing sustainable employee performance. The results of this study are expected to provide both theoretical contributions to organizational management studies and practical references for organizations in designing effective management systems.  

The increasing adoption of collaborative robots in modern manufacturing environments requires reliable perception systems that can ensure both safety and operational efficiency during human–robot collaboration. This study proposes a CNN-based real-time computer vision system for object and human detection in shared robotic workspaces. The research focuses on developing and evaluating a single-stage deep learning detection model optimized for real-time performance while maintaining high detection accuracy. The proposed methodology includes dataset preparation, model training using transfer learning, real-time system implementation, and comprehensive performance evaluation. Experimental results demonstrate that the developed system achieves high detection accuracy, as reflected by strong precision, recall, and mean Average Precision (mAP) values, while maintaining low inference latency suitable for real-time operation. The system consistently operates above real-time frame-rate thresholds, ensuring timely perception updates required for safety-related decision-making in collaborative robotic environments. Graphical and quantitative analyses further confirm the stability of inference performance under dynamic interaction scenarios involving human movement and multiple objects. Compared with existing approaches, the proposed system provides a balanced trade-off between accuracy and computational efficiency, making it practical for deployment in safety-aware human–robot collaboration scenarios. Overall, the findings indicate that CNN-based real-time object detection systems can effectively support perception and situational awareness in collaborative robotics, contributing to safer and more efficient industrial automation.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

This study explores the integration of adaptive streaming models with edge computing to optimize multimedia delivery, particularly in real-time applications such as video conferencing, live streaming, and virtual reality. The proposed model leverages adaptive compression techniques, including scalable video coding (SVC) and hybrid adaptive compression (HAC), which adjust video quality based on real-time network conditions. The use of edge computing further enhances the model by processing and delivering content closer to the user, reducing latency and optimizing bandwidth usage. The research demonstrates that the edge computing-based adaptive streaming model significantly improves latency by up to 30%, reduces bandwidth consumption, and ensures higher visual quality during video playback, even under fluctuating network conditions. This model addresses key challenges in multimedia streaming, such as maintaining video quality in bandwidth-constrained environments and minimizing buffering times. Furthermore, it enhances the overall Quality of Experience (QoE) for users by providing smoother interactions and real-time responsiveness. The study highlights the potential impact of this model on various fields, including remote education, entertainment, and interactive applications, where low-latency content delivery and high-quality streaming are critical. The findings suggest that integrating AI algorithms for even more efficient compression and expanding edge computing infrastructures will further optimize multimedia streaming in the future, ensuring reliable and high-quality user experiences in increasingly connected environments.

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.

The development of digital marketing has become a strategic factor in the growth of creative culinary businesses, particularly for micro, small, and medium enterprises facing increasing competition and changes in consumer behavior in the digital era. The use of digital platforms, such as social media and online marketplaces, enables business owners to expand market reach, enhance customer interaction, and optimize promotional strategies more efficiently. Nevertheless, various studies indicate that the implementation of digital marketing has not always produced optimal outcomes due to limitations in digital literacy, human resource capacity, and organizational adaptability. This study aims to systematically examine the role of digital marketing in the development of creative culinary businesses through a Systematic Literature Review approach. The review maps key concepts, research trends, and empirical findings related to digital marketing and organizational learning within the context of creative culinary enterprises. In addition, this study identifies existing research gaps and formulates recommendations for future research issues. The findings are expected to provide theoretical contributions in the form of comprehensive conceptual mapping as well as practical contributions for business practitioners, MSME facilitators, and policymakers in designing adaptive and sustainable digital marketing strategies.

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 Indonesia Smart Program (Program Indonesia Pintar/PIP) is a government initiative aimed at ensuring equal access to education for students from underprivileged families, including those at the junior high school (SMP) level. However, at the school level, the management of PIP recipient data still faces several challenges, particularly in data searching and utilization, due to the increasing volume of data and the use of simple or manual search methods. These conditions can lead to delays in obtaining information and reduce the accuracy of decision-making. Therefore, an effective information retrieval system is needed to manage and search PIP recipient data efficiently. This study aims to design and develop an Information Retrieval System for PIP recipient data at the junior high school level using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The TF-IDF method is applied to assign weights to terms in each document, enabling the system to identify and rank documents based on their relevance to user queries. The test results show that the system is able to measure document relevance accurately, where documents D3 and D4 obtain the highest similarity value of 0.099586089 and are classified as highly relevant, while other documents show lower similarity values down to zero. These results are also supported by graphical visualization, which helps users compare relevance levels more clearly. Thus, the implementation of the TF-IDF method has proven to be effective in supporting accurate, efficient, and systematic searching and management of PIP recipient data at the junior high school level.

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.

Digital transformation in public service delivery encourages government to implement service innovations that are effective, efficient, transparent, and accountable. This transformations is realized through the adoption of innovations capable of responding to public needs in a timely and accurate manner to improve service quality. The Regional Civil Service Agency of East Java Province developed the Rumah ASN Application as a digital-based personnel service innovation to support the needs of civil servants of the East Java Provincial Government and the general public. This study aims to analyze and describe the implementation of the Rumah ASN Application as an innovation in personnel services at the Regional Civil Service Agency of East Java Province. This research employs a qualitative descriptive. The analytical framework is based on the public sector process innovation theory proposed by Khodadad-Saryazdi (2022), which consists of seven key success factors: strategic alignment, governance, leadership, culture, information technology and information system, process actors, and performance evaluation. Data were collected through interviews, observations, and documentation. The findings indicate that the implementation of the Rumah ASN Application has generally been conducted well, but it has not yet reached optimal. Challenges identified for optimizing this service including the needs for continuous user socialization during system updates, optimization of service features for civil servant capacity building, strengthening administrative capacity and cross-sectoral coordination, and the developing the application into a mobile application version.