The structure of the Windows operating system plays an important role in supporting the operational performance of computers, especially in business environments and professional users. This system is designed with a modular and layered architecture, which includes various components such as Hardware Abstraction Layer (HAL), I/O device manager, virtual memory management, and an integrated security system. Windows also supports energy management and plug-and-play mechanisms, which improve power efficiency and hardware compatibility. This architectural approach allows Windows to optimize interactions between hardware and applications, supports multitasking, and provides an efficient user interface. Thus, the structure of the Windows operating system is able to significantly improve device performance, which leads to optimization of operational performance.

This research compares the techniques used in the file system structures of Windows and Linux. Each operating system employs distinct approaches to data organization, access, and management, as reflected in their respective file systems: NTFS for Windows and ext4 for Linux. The study explores the strengths, weaknesses, and applications of these systems under various conditions. By analyzing both theoretical documentation and empirical experiments, this research highlights the technical and philosophical aspects influencing the design and performance of file systems in Windows and Linux. The findings provide insights for selecting an appropriate file system based on specific requirements, emphasizing performance, reliability, and usability.

As a popular platform, Windows faces increasingly complex and diverse malware threats. This research evaluates Windows security weaknesses and finds effective methods of mitigating malware attacks. To ensure the validity of the results, the analysis was conducted through literature review, protection method evaluation, and data triangulation. The results show that layered protection methods, which include implementing firewalls, antivirus, and regular system updates, are highly effective in combating different types of malware, such as ransomware, trojans, and spyware. In addition, the use of machine learning and artificial intelligence helps detect threats in real-time. This research provides practical advice for developers and users on how to improve Windows security through a combination of proactive and reactive measures. It also suggests that users be more aware of cybersecurity. The results of this research are expected to serve as a reference for the development of more secure operating systems in the future.

The debugging process is a critical step in software development, especially for the Windows operating system, which offers various tools and techniques to address software issues. This study explores debugging strategies such as logging, memory analysis, real-time debugging, reverse engineering, and compilation-based techniques to identify and resolve problems such as memory leaks and unresponsive applications. Results indicate that selecting the right debugging method depends on the type and complexity of the problem, with a combination of methods often yielding the best outcomes. This study provides practical guidance for Windows users to enhance debugging effectiveness in resolving system and application issues.

Assembling a personal computer and installing the Windows operating system are important activities in mastering information technology. This research aims to provide an in-depth understanding of the computer assembly process and operating system installation. The methods used include a Research and Development (R&D) approach, hands-on practice, and question-and-answer sessions. The research results show that systematic assembly steps and proper installation can produce a well-functioning computer. In addition, initial testing after assembly is crucial to ensure that all devices are detected correctly. It is hoped that this research can serve as a reference for information technology education and enhance students' practical skills in the field of computers.

Task scheduling in cloud computing environments is a crucial aspect in optimizing resource allocation and improving system efficiency. This research aims to analyze trends in task scheduling algorithms in cloud computing using a Systematic Literature Review (SLR) approach on various scientific publications published between 2018 and 2025. The results of the study show that Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) algorithms are the most commonly used methods in solving task scheduling problems. PSO stands out as an effective algorithm due to its ability to find global optimal solutions, handle non-linear and multimodal problems, and its efficiency in managing computational resources. Additionally, various studies have shown that optimization of scheduling algorithms can be achieved through a combination or modification of existing methods to improve system performance. This study provides in-depth insights into the development of scheduling algorithms in cloud computing and opens up opportunities for further research in developing more innovative and adaptive approaches.