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This study investigates the relationship between design patterns, modular architecture, and the maintainability of distributed real time systems developed using agile practices. Distributed real time systems are critical in various sectors, including telecommunications, healthcare, and automotive, where strict timing constraints and reliability are essential. Agile methodologies, known for their flexibility and iterative development, have been widely applied to software engineering, but their impact on long-term system maintainability, especially in complex real time environments, has been insufficiently explored. This research employs an empirical analysis, combining both quantitative and qualitative data from multiple real time system projects using agile methods. The analysis focuses on the application of design patterns, such as Singleton, Observer, and Factory, and evaluates the effectiveness of modular architectures in enhancing system scalability, flexibility, and long-term sustainability. The study also explores how agile practices contribute to system performance and maintainability, despite challenges related to frequent updates and coordination among distributed teams. Key findings show a positive correlation between the consistent use of design patterns and modularity, which significantly improves the maintainability and adaptability of distributed real time systems. This research also highlights the challenges faced by agile methods in maintaining architectural consistency and managing non-functional requirements, particularly in distributed environments. The results contribute valuable insights into adapting agile practices to meet the specific demands of distributed real time systems, offering recommendations for developers and project managers to incorporate modular architecture and design patterns to enhance long-term system sustainability. Further research is suggested to explore new design patterns and investigate the broader impact of agile methodologies on system quality beyond maintainability.

This study compares the scalability and maintainability of three prominent programming paradigms-functional programming (FP), object-oriented programming (OOP), and declarative programming (DP)-in the context of distributed data processing systems. The research aims to evaluate how each paradigm performs under increased data volume and its ability to handle complex operations, while also assessing the ease of maintenance through code readability, modularity, and the flexibility of updating and debugging. The study employs a comparative experimental design, implementing identical data processing tasks, such as data aggregation, filtering, and transformation, across each paradigm. Key findings indicate that FP and DP outperform OOP in terms of scalability, with their stateless nature and high-level abstractions enabling efficient parallel processing and task distribution. FP, with its emphasis on immutability and concurrency, and DP, with its focus on describing desired outcomes rather than implementation specifics, both demonstrate superior performance in handling large datasets. However, while OOP excels in modularity and flexibility, its reliance on mutable state and shared resources hampers its scalability in distributed environments. In terms of maintainability, both FP and DP offer clearer, more maintainable code due to their abstraction levels, making them easier to update and extend. OOP, while modular, presents challenges in managing mutable state, complicating maintenance. This paper concludes with practical recommendations for developers on when to use each paradigm based on system requirements and suggests areas for future research, such as hybrid paradigms and long-term maintainability studies in real-world applications.

The limited urban land in Greater Jakarta demands the emergence of a residential approach that is more adaptive to the growing needs of space. This study examines the application of the concept of a growing house in the residence of Rumah Swatantra by Studio Dasar. The research method uses a qualitative-descriptive approach through literature studies and direct interviews with the designer architects. The results of the study show that the application of modular patterns and concrete hollow infill structure systems provides the flexibility of space growth without the need to dismantle the basic structure. The process of space growth occurred gradually following the development of the family from 2017 to 2024, and showed that the quality of space can still be maintained even though the building is built in stages. These findings confirm that the concept of growing houses can be an alternative strategy for sustainable housing in urban areas that experience limited land. In addition, this study highlights the importance of careful spatial planning from the early stages of development so that spatial expansion can be carried out efficiently, measurably, and in harmony with the needs of residents. This approach not only improves the comfort and functionality of the space, but also minimizes construction waste and future renovation costs.

This research describes the flexibility and simplicity of the Linux operating system architecture. These are the main factors behind its popularity in various technological applications. This study uses a literature approach to explore kernel modularity, extensive file system support, and kernel modularity in meeting the needs of users in various computing environments, ranging from the Internet of Things (IoT) to large-scale servers. Research shows that the modular structure of Linux makes it easy to customize the operating system without compromising stability. In addition, the open source nature of Linux supports learning and innovation among the global community. Despite the challenges of development complexity, the open source community remains committed to providing documentation and tools to support the user experience. With flexibility and simplicity at the core of its design, Linux continues to adapt to the rapid advancement of information technology.

Artificial Intelligence (AI)-based Decision Support Systems (DSS) have become a central component of digital transformation initiatives across various industries. While prior studies have primarily emphasized technical aspects such as accuracy, performance, and computational efficiency, less attention has been given to the integration of human-centered principles and scalable architectural design. This study aims to examine how AI-based DSS can be enhanced through the combined application of Human-Centered Artificial Intelligence (HCAI) principles and scalable AI architecture. Using a qualitative, literature-based research methodology, this study systematically analyzes peer-reviewed publications indexed in Scopus to identify key dimensions influencing the effectiveness and sustainability of AI-driven DSS. The findings indicate that technical capabilities alone are insufficient to ensure successful adoption and long term impact. Instead, transparency, explainability, ethical governance, and user empowerment core elements of HCAI are critical for fostering trust and user acceptance. Furthermore, scalable architectural principles, including modularity, interoperability, and adaptability, are essential for enabling AI-based DSS to operate reliably in large-scale and dynamic environments. This study contributes a unified conceptual framework that bridges technical scalability and human-centered design, offering theoretical insights and practical guidance for developing trustworthy, scalable, and sustainable AI-based Decision Support Systems in digital transformation contexts.

In an effort to increase productivity to ensure smooth production processes, it is necessary to implement an appropriate maintenance system to reduce production machine downtime. The purpose of company maintenance is to keep production machines in good condition so that production operations can run efficiently and effectively. PT Yale Woodpallet Indonesia is a company in Indonesia that produces wooden pallets (wood pallet). This company often experiences problems with damage to machines due to suboptimal machine maintenance because the maintenance system is carried out to repair damaged components with new components resulting in downtime. Among these problems, this study aims to determine maintenance intervals to minimize maintenance costs by using modularity design and age replacement methods. Based on the analysis of the results obtained, it is known that the modularity design and age replacement methods can achieve optimal component replacement intervals for pallet machine components is 30,000 minutes, rotary screening components are 33,200 minutes and hopper components are 33,200 minutes. The total maintenance cost per year is Rp. 2,115,610,959, -. compared to the company's maintenance costs which have a value of Rp. 2,668,692,000, the maintenance system using the modularity design and age replacement method is more efficient by 1.27%.

PT. Petrosida is a manufacturing company engaged in pesticide production. The production system in this company is Make To Order (MTO) where the company will produce products after an order or order from consumers. In its production PT. Petrosida has problems with the main tools used for packaging, one of which is a 1-liter induction semi-automatic filling machine in the herbicide unit, where treatment is carried out after damage to a component. Critical components in filling machines experience an average of 20 times damage in 2022-2023 and the machine maintenance process is still not optimal with an average downtime of 5 hours / month. Therefore, the purpose of research is to be able to carry out preventive maintenance using the modularity design method in the hope that there will be no more breakdowns that hamper the production process so as to reduce machine maintenance costs and to find out the minimum total machine maintenance cost planning using the life cycle cost (LCC) method. By implementing preventive maintenance with modularity design, the total maintenance cost of Rp. 9,747,782 is lower than the company's total maintenance cost of Rp. 23,023,176 with an efficiency rate of 42.3%. And obtained the lowest total life cycle cost of Rp. 340,459,348, - with an optimal life on the machine of 3 years & the number of mechanics one person.