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This study aims to address the challenges and propose solutions for the Optimization of Blockchain-Based Cybersecurity Systems to Enhance Resilience Against Ransomware Attacks using a Systematic Literature Review (SLR) approach. Blockchain is increasingly recognized as a transformative technology in cybersecurity due to its decentralized structure, transparency, and robustness in securing data. Despite these advantages, its widespread adoption is hindered by several challenges, including scalability, interoperability, high energy consumption, and limited access to representative ransomware datasets. This research highlights that integrating blockchain with advanced technologies such as data analytics, machine learning, and Explainable AI (XAI) can significantly enhance its effectiveness in combating ransomware.The findings reveal that Graph Convolutional Neural Networks (GCN) enable real-time detection of ransomware patterns in network traffic with an accuracy of up to 95%. Furthermore, Layer-2 solutions like the Lightning Network and sharding effectively alleviate the load on main blockchains, thereby increasing transaction throughput. Efficient consensus mechanisms, including Proof of Stake (PoS) and Delegated Proof of Stake (DPoS), address energy consumption issues, making blockchain more adaptable to IoT and resource-constrained environments. These approaches have proven successful in enabling early detection, mitigation, and prevention of ransomware in IoT systems, cloud infrastructures, and smart grid networks. The implications of this study underscore the potential of blockchain as a critical component of proactive and adaptive cybersecurity systems. However, overcoming existing challenges requires further development of hybrid frameworks that integrate blockchain with data analytics and machine learning technologies. In addition, efforts should focus on standardizing global security protocols to enhance interoperability and creating robust, diverse ransomware datasets to support more accurate detection systems. Future research should also explore methods to minimize latency and improve blockchain efficiency in real-time cybersecurity applications.

The application of algorithms in the mentoring activity scheduling system is an innovative solution to overcome the complexity of time and resource management. This study aims to develop a mentoring activity scheduling system for Reading and Writing the Qur'an (BTQ) using the particle swarm optimization (PSO) algorithm. The PSO algorithm was chosen because of its ability to find optimal solutions efficiently through a particle population approach. This system is designed to meet the schedule preferences of students and supervisors, taking into account the limited time available and the interrelationships between schedules. This study makes a significant contribution to improving the efficiency of BTQ mentoring activity scheduling, as well as demonstrating the potential of PSO in solving other scheduling problems.

The optimization of thread management in operating systems is essential to improve the performance of multithreading-based applications. This study analyzes various techniques to enhance thread management efficiency, including thread pooling, dynamic scheduling, load balancing, and synchronization contention reduction. Experimental results indicate that thread pooling reduces overhead by reusing threads, while dynamic scheduling improves multitasking responsiveness by prioritizing urgent tasks. Load balancing ensures equitable workload distribution across processor cores, minimizing execution time. Furthermore, reducing synchronization contention using mechanisms like semaphores decreases thread waiting time, thereby boosting application performance. These optimizations enable modern operating systems to efficiently utilize multi-core architectures, improving speed, responsiveness, and user experience. This research highlights the importance of thread management optimization as a foundational step towards developing adaptive and scalable operating systems for future complex and data-intensive applications.

Quantum sensing technologies are revolutionizing precision measurements in electrical engineering by leveraging quantum phenomena to achieve unprecedented accuracy. This paper examines the applications of quantum sensors in electric field detection, biomedical instrumentation, and power grid monitoring. The study explores their ability to enhance sensitivity, minimize noise interference, and improve real-time data acquisition. Through advanced quantum principles, these sensors enable high-resolution diagnostics in medical devices, optimize energy efficiency in smart grids, and enhance electromagnetic field measurements. The findings highlight the transformative potential of quantum sensors in modern engineering applications, paving the way for smarter and more efficient monitoring systems.

The Internet of Things (IoT) has become one of the leading technologies in digital transformation, including in customer relationship management (CRM). This article discusses how IoT integration in CRM systems can improve customer satisfaction and retention through real-time data collection, service personalization, and data-driven decision making. With a literature study and implementation analysis, this research shows that IoT technology can provide deeper insight into customer behavior, improve operational efficiency, and create a more satisfying customer experience.

Computer vision-based algorithms, especially Convolutional Neural Networks (CNN) and You Only Look Once (YOLO), have become the leading approaches in plant disease detection. CNN excels in extracting complex visual features for disease classification, while YOLO provides high-efficiency real-time object detection capabilities. Both algorithms have shown promising results in various studies, especially with controlled datasets. However, challenges remain in their application in real-world conditions, such as environmental diversity, overlapping symptoms, and poorly annotated data. Future research has the potential to optimize these algorithms through the development of lighter models, the use of transfer learning techniques, and multi-modal data integration. In addition, further exploration of a wider range of diseases, crops, and environmental conditions can expand the application of these algorithms. By leveraging these innovations, computer vision-based plant disease management can be improved to support sustainable precision agriculture.

This research is a Systematic Literature Review (SLR) aimed at analyzing the application of Artificial Intelligence (AI) technology in the management of information technology (IT) projects. This study focuses on identifying the AI technologies employed, the benefits gained, and the challenges faced in implementing these technologies. The study gathers and analyzes literature from various leading databases, including Scopus, IEEE Xplore, and SpringerLink, within the timeframe of 2015–2025. The findings reveal that AI technologies such as machine learning, predictive analytics, and natural language processing play a significant role in improving efficiency, reducing risks, and supporting decision-making in IT project management. However, challenges such as data quality, organizational resistance, and implementation costs remain major obstacles in adopting this technology. This review provides comprehensive insights into trends, benefits, and barriers associated with AI utilization, along with recommendations for more effective implementation in the future.

The rapid development of multimedia technology has significantly advanced 3D animation techniques, enabling the production of high-quality visual content across industries such as film, gaming, architecture, and product visualization. Rendering, as the final stage of the 3D production pipeline, plays a crucial role in determining both visual realism and production efficiency. This study compares the performance of three rendering engines—Eevee, Cycles, and Radeon ProRender—by evaluating rendering speed, visual quality, and memory efficiency in Blender. The objective is to provide practical insights for designers and digital content creators in selecting the most suitable rendering engine based on project requirements. In this research, three identical 3D scenes were rendered using each of the three rendering engines under controlled experimental conditions. The comparison was conducted based on several parameters, including rendering time, output file size, shadow accuracy, lighting effects, and overall visual realism. Quantitative measurements were used to evaluate render speed and memory consumption, while qualitative analysis assessed differences in shadow detail, global illumination behavior, reflection accuracy, and material realism. The results indicate that Eevee outperforms the other engines in terms of rendering speed, making it highly suitable for real-time applications and projects requiring fast previews. Cycles produces the highest level of visual realism due to its physically based path-tracing algorithm, although it requires longer rendering time and higher computational resources. Meanwhile, Radeon ProRender demonstrates competitive performance, particularly in shadow quality and lighting effects, offering a balanced alternative between realism and efficiency. Based on the findings, Blender remains a flexible and effective platform. The choice of rendering engine should depend on whether speed, graphic quality, or memory optimization is prioritized.

This research focuses on developing a cayenne pepper quality detection system using the YOLO (You Only Look Once) V5 algorithm. The system is designed to address the limitations of manual post-harvest sorting by classifying cayenne peppers into three categories: “good” (ripe), “bad” (rotten or dry), and “raw” (green), based on their visual characteristics. A dataset consisting of 565 images was manually collected, labeled using Roboflow, and pre-processed through resizing and orientation standardization. Model training was conducted over 150 epochs, achieving high detection performance with a mean average precision (mAP) of 99.5%, precision of 99.6%, and recall of 99.9%. Real-time testing demonstrated the system’s capability to detect and classify cayenne peppers with exceptional accuracy. This research is expected to enhance the efficiency and accuracy of the cayenne pepper sorting process, while paving the way for the application of YOLO-based systems to other agricultural commodities. Further research is recommended to expand the dataset and optimize model parameters for improved system performance.

The National Health Insurance Program (JKN) organized by BPJS Health aims to improve access and quality of health services for all levels of Indonesian society. This research examines the effectiveness of the JKN program, with a focus on the use of the JKN mobile application, program implementation in health facilities, and factors that influence service quality. Based on case studies in several regions, the research results show that the JKN mobile application makes it easy for participants to access health-related services and information, although there are still obstacles such as a lack of public understanding of the application and technical problems. Apart from that, health services in hospitals and community health centers still face challenges such as a shortage of medical personnel, delays in services, and timeliness in administrative processes. Although the JKN program has expanded access to services, challenges in equal distribution of service quality and health resources still need to be overcome. Overall, the JKN program plays an important role in improving people's health and welfare, but improvements are needed in terms of community understanding, infrastructure and service efficiency to achieve more optimal and equitable health goals throughout Indonesia.

Fruit cultivation plays a crucial role in the agricultural economy, where seed quality significantly impacts productivity. Watering and fertilizing fruit plant seedlings are key aspects of ensuring healthy growth. To meet the demands of modern agriculture, an automated solution has been developed utilizing messaging application-based technology, such as Telegram. This device enables remote control of watering and fertilizing processes via the Telegram bot feature, ensuring precision and convenience. Advantages include time efficiency, optimal fertilization, and precise watering, supported by light sensors and soil moisture sensors to enhance the process. Experimental results show that this technology successfully addresses challenges related to irregular watering and fertilization. Consequently, this automated system has the potential to enhance productivity and efficiency in fruit cultivation.

The rapid development of information technology and telecommunications has driven companies and MSMEs to adopt desktop, mobile, and web-based applications to enhance operational performance. PT HPS Painan Padang, previously relying on manual ticket bookings, faced challenges in efficiency and susceptibility to brokering practices. To address this, a web-based travel ticket booking application was developed, enabling customers to book tickets online more practically, quickly, and conveniently, anytime and anywhere. The integration of Natural Language Processing (NLP) technology in the form of a chatbot further enhances service efficiency and responsiveness. This feature provides real-time information on departure schedules, seat selection, and payment transactions while also supporting sentiment analysis and swift responses to customer feedback, ultimately improving service quality. The application, built using PHP, MySQL, and CodeIgniter with a Prototype development approach, adapts to changing market demands. Testing via GTmetrix on the https://desistravel.xyz/backend page demonstrated excellent performance with an 88% Performance score, 95% Structure score, and optimal Web Vitals metrics, indicating a fast, responsive, and stable platform. The final product is a user-friendly web-based ticket booking application supported by an intelligent chatbot, offering a seamless user experience, enhancing customer satisfaction, and streamlining the travel booking process efficiently.

The on the spot mobile sub-district service program (POSKAMLING) is an innovative service in Gedangan sub-district, Sidoarjo Regency which aims to bring direct services closer to the community in the form of mobile services. This article aims to determine the Evaluation of the POSKAMLING Program (On The Spot Mobile Sub-district Service) in Gedangan Sub-district, Sidoarjo Regency. The problem is focused on Program Evaluation. The data analysis uses Milles' analysis technique. Hubernman and Salda 2014. The theoretical reference used is William N Dunn's theory (2003), which consists of Effectiveness, Efficiency, Adequacy, Leveling, Responsiveness, and Accuracy. The informants of this study involved key informants including the Gedangan Sub-district Computer Administrator and the Population and Civil Registration Service Staff in the Population Administration Information System Management Section. Based on the research results, the POSKAMLING Program is said to be very effective and efficient because the service makes it easier for the community. This program has proven to be right on target and satisfies community groups. However, in the efficiency criteria there are shortcomings and obstacles in the implementation of the POSKAMLING Program such as late delivery of forms. So that makes people wait a long time in the process of managing population documents.

This article discusses efforts to optimize the production process at the motorcycle carburetor company "Mantri Karbu" through the application of transformation methods. In the automotive industry, production efficiency is key to enhancing competitiveness and meeting the ever-growing market demand. This study identifies various challenges faced by Mantri Karbu, including long production times, resource waste, and inconsistent product quality. By implementing transformation methods, which include workflow analysis, waste reduction, and employee skill enhancement, the company can improve productivity and operational efficiency. The results of this implementation show a significant increase in production cycle time, a reduction in operational costs, and an improvement in customer satisfaction. This article aims to provide insights for other companies in similar industries to adopt effective strategies for optimizing their production processes.

This study discusses the implementation of the Excise Services Information System (ExSIS) application at the Sidoarjo Customs and Excise Office as an innovation in public services. The objective is to evaluate the effectiveness of ExSIS in improving operational performance and provide recommendations for its development. This research employs a qualitative method with interviews conducted with Customs and Excise officers and service users, analyzed using Everett M. Rogers' diffusion of innovation theory. The results indicate that ExSIS improves time efficiency by up to 80% and reduces administrative errors by 70%. However, challenges such as user adaptation and the need for adequate infrastructure remain. In conclusion, ExSIS has enhanced efficiency and transparency in Customs and Excise administration. The study recommends continuous training, infrastructure upgrades, and internal policy revisions to maximize the application’s potential in supporting faster and more accurate public services.

This study aims to analyze the performance of the District Election Committee in Ciwandan District, Cilegon City. A qualitative descriptive approach was employed, with performance measured based on (S. Robbins et al., 2013) criteria, which include quality, quantity, timeliness, effectiveness, and efficiency. Data were collected through observation, interviews, and documentation, and analyzed using the stages of Data Condensation, Data Display, and Conclusion Drawing.The results indicate that the performance of the District Election Committee during the 2024 simultaneous general elections in Ciwandan District was generally in accordance with the planned schedule. However, several issues were identified, such as logistical shortcomings, including the insufficient supply of ballot papers and instances of ballot exchanges with other polling stations during the election process. Furthermore, 97 individuals were found to be unregistered in the voter list, indicating inadequacies in voter list updates a key responsibility of the District Election Committee. The study also highlights the insufficient commitment of committee members, as some members had other jobs outside the committee, leading to suboptimal performance due to task delegation to less knowledgeable members.

The optimization of distribution costs from the Koe Bluder factory to various branches is the problem in this study, which aims to minimize total delivery costs and maintain service quality and time efficiency. In the context of the food industry, especially the increasingly popular bluder cake, effective distribution management is crucial to meet customer demand without increasing operational costs. This study uses transportation methods to analyze and optimize the distribution process, which includes determining the initial allocation and identifying factors that affect transportation costs. The approaches used include the North West Corner (NWC) and Modified Distribution Method (MODI) methods to allocate and minimize shipping costs. The analysis results show that a total transportation cost of Rp 124.825,000 can be achieved using both methods. This research highlights the importance of efficient distribution planning in improving operational efficiency and customer satisfaction, with an emphasis on reducing costs without sacrificing service quality

Tiga Putra's workshop, which is located in Bojonegara Banten, still has meetings with the large number of requests for vehicle repairs from customers, making the workshop have to provide various kinds of spare parts. The purpose of making this journal is to find out the existing conditions in the three sons' workshop and we provide solutions on how to increase the supply of spare parts. The aim of increasing the inventory of motorbike spare parts is to ensure the availability of components needed for vehicle maintenance and repair, so as to increase operational efficiency and customer satisfaction. The methodology used is a qualitative case study, with data collection through in-depth interviews, documentation studies and field observations. The results of the research are that there are obstacles in increasing the supply of motorbike spare parts, namely limited resources, be it money, time or labor. And Tiga Putra's workshop also has a strategy to increase spare parts inventory which can help with real-time inventory and plan reorders more efficiently. And there is also a spare parts inventory table so you can find out which spare parts orders are sold out within one year and require delivery.

Objective: This study investigates the critical role of risk management practices in enhancing corporate performance, with a specific focus on financial institutions and manufacturing sectors. The research aims to uncover how effective risk management contributes to organizational resilience, operational efficiency, and long-term sustainability.Methodology: A Systematic Literature Review (SLR) methodology was employed to analyze 11 peer-reviewed academic articles published between 2015 and 2023. The selected studies were drawn from reputable databases such as Scopus and Web of Science, using predefined inclusion criteria. The analysis focused on identifying patterns, gaps, and emerging trends in risk management practices.Findings: The study reveals that robust risk management significantly enhances organizational resilience, mitigates exposure to financial and operational risks, and fosters improved corporate governance. Frameworks such as ISO 31000 and Enterprise Risk Management (ERM) are identified as pivotal in aligning risk management with strategic objectives, thereby driving sustainable growth.Contribution: This research underscores the need for a unified and standardized risk management framework that addresses sector-specific challenges while promoting cross-industry applicability. Policymakers and industry leaders are urged to prioritize risk management as a strategic imperative, especially in times of crisis.

Electronic Health Records (EHRs) have become an important component in the digital transformation of the healthcare system in Indonesia. However, EHR implementation in many health facilities in Indonesia is still fragmentary and not fully integrated. The main challenges in integrating health data into the national health system are issues of interoperability, data security, and inter-institutional coordination. This study aims to identify and analyze the main challenges in integrating health data from various EHR systems into Indonesia's national health system and its impact on the quality of health services, operational efficiency, and data security. The research method used was a descriptive qualitative approach, which combined literature study and in-depth interviews with experts in the field of health information technology and health policy. A literature study was conducted to identify technical, operational, and policy challenges faced in EHR implementation in Indonesia. Expert interviews were used to validate the findings obtained from the literature and provide additional insights into the local context. Thematic analysis techniques were used to organize and interpret the data. The results showed that the lack of common data standards is a major obstacle in the integration of EHR systems in Indonesia.A total of 45% of healthcare facilities adopt HL7 standards, 30% adopt FHIR, and 25% use proprietary systems, leading to the inability to efficiently share data between institutions. In addition, data security issues are also a major concern, with many healthcare facilities yet to implement adequate security protocols. Limited coordination between health institutions also hinders wider integration. Nonetheless, EHR integration has the potential to improve operational efficiency, with up to 25% reduction in administrative time in institutions that have integrated their systems.