The Eastasouth Journal of Information System and Computer Science

Blockchain for Monitoring the Safe Supply Chain of Medicines: Reducing the Circulation of Illegal Drugs in Indonesia

Ismail Ismail, Felecia Felecia, Anisa Kurniatul Azizah, Diana Rahmawati — Fri, 11 Apr 2025 00:00:00 +0000

The pharmaceutical supply chain in Indonesia faces significant challenges, including counterfeit drug infiltration and insufficient traceability. This study explores the application of blockchain technology as a solution to monitor and secure the supply chain, reducing the circulation of illegal drugs. Using a qualitative approach, data were collected from five informants, including experts in blockchain technology, pharmaceutical supply chain management, and regulatory oversight. The findings reveal blockchain’s potential to enhance transparency, accountability, and regulatory compliance through features such as decentralized ledgers and smart contracts. However, challenges such as high implementation costs, technical barriers, and regulatory gaps hinder its adoption. The study concludes with recommendations, including capacity building, collaborative pilot programs, and the development of supportive policies, to facilitate blockchain integration in Indonesia’s pharmaceutical sector.

Social Media as an Early Warning System for Drug Prevention: Case Study of Surabaya's Digital Surveillance Program

Ismail Ismail, Felecia Felecia, Anisa Kurniatul Azizah, Diana Rahmawati — Fri, 11 Apr 2025 00:00:00 +0000

Social media has emerged as a powerful tool in public health initiatives, including drug prevention efforts. This study examines the role of social media as an early warning system in the Digital Surveillance Program in Surabaya. Using a quantitative approach with a sample of 100 respondents, data was collected through a Likert-scale questionnaire (1–5) and analyzed with Structural Equation Modeling - Partial Least Squares (SEM-PLS). The results reveal that public awareness significantly influences social media engagement and program effectiveness, while social media engagement plays a critical role in enhancing program outcomes. The structural model demonstrates moderate predictive power (R² = 0.53 for program effectiveness), highlighting the importance of integrating public awareness campaigns and social media engagement strategies to optimize drug prevention initiatives. The findings provide actionable insights for policymakers and stakeholders, emphasizing the need for community-centered content creation and data-driven social media strategies to combat drug misuse.

Identifying and Managing Noisy Neighbors in Multi-Tenant PostgreSQL Deployments (On-Premise)

Murali Natti — Wed, 30 Apr 2025 00:00:00 +0000

In on-premise multi-tenant PostgreSQL deployments, multiple tenants share the same physical infrastructure to maximize resource utilization and reduce operational costs. However, this shared environment can give rise to significant resource contention, particularly when one tenant exhibits the “noisy neighbor” effect—where its workload consumes a disproportionate amount of CPU, memory, or disk I/O relative to others. This unbalanced resource consumption can lead to widespread performance degradation, manifesting as increased query latency, reduced throughput, and overall service instability. The present article investigates the challenges inherent in multi-tenant setups, focusing on the detection and management of noisy neighbors. It explores both native PostgreSQL monitoring techniques (such as system views and performance statistics) and external solutions including Linux control groups (cgroups) for isolating and limiting resource usage. Additionally, the article outlines best practices for proactive monitoring, query optimization, and resource allocation to ensure a balanced and efficient multi-tenant environment. By providing a comprehensive framework for understanding and mitigating the noisy neighbor phenomenon, this work aims to equip database administrators and system architects with effective strategies for maintaining robust performance, even under heavy and unevenly distributed workloads.

Emerging Research Trends in Natural Language Processing for Multilingual AI

Loso Judijanto, Arnes Yuli Vandika — Wed, 30 Apr 2025 00:00:00 +0000

This study explores the emerging trends and developments in Natural Language Processing (NLP) for Multilingual Artificial Intelligence (AI) through a comprehensive bibliometric analysis. Drawing on data from the Scopus database spanning 2013 to 2023, the research identifies key publication patterns, influential contributors, thematic clusters, and collaboration networks that shape the evolution of multilingual NLP. The analysis reveals a significant increase in research activity over the past five years, particularly driven by advancements in deep learning and the emergence of multilingual pretrained models such as mBERT and XLM-RoBERTa. Institutions from the United States, India, and China lead the global research landscape, while collaborative clusters highlight the interdisciplinary and international nature of the field. Keyword analysis shows a paradigm shift from rule-based and statistical approaches to neural and transformer-based architectures, with increasing application in healthcare, social media, and big data environments. Despite this growth, the study identifies ongoing challenges, including disparities in language representation, bias in model training, and the need for ethical and inclusive research practices. The findings provide a strategic overview for researchers, policymakers, and practitioners aiming to advance equitable and effective multilingual AI systems.

TLENVOY - A Tool for TLS Termination and Inspection

Udit Misra — Wed, 30 Apr 2025 00:00:00 +0000

With the increasing adaption on encrypted communication over the internet, ensuring the security over network traffic has become very crucial. Transport Layer Security (TLS) is now widely used to secure data in transit, but at the same time it also poses challenges for network administrators who need to inspect traffic for malicious content or policy violations. This paper explores the use of Envoy, an open-source edge and service proxy, as a forward proxy to inspect TLS traffic. By leveraging Envoy's capabilities, organizations can maintain a secure environment for all nodes behind the proxy. We discuss the architecture, implementation, security considerations, and potential challenges of using Envoy for TLS inspection. The paper concludes with recommendations for deploying such a system in a secure and efficient manner.

Research Advancements in Digital Twin Technology for Smart Manufacturing

Loso Judijanto, Arnes Yuli Vandika — Wed, 30 Apr 2025 00:00:00 +0000

This study presents a comprehensive bibliometric analysis of research developments in Digital Twin (DT) technology within the domain of smart manufacturing. Drawing on Scopus-indexed publications from 2010 to 2024, the study explores the growth patterns, thematic structures, institutional contributions, collaborative networks, and emerging research trends using VOSviewer. The findings reveal a sharp increase in publication volume, particularly in 2024, indicating growing academic and industrial interest. China dominates the research landscape in terms of both institutional productivity and international collaboration, followed by India and the United States. Keyword co-occurrence analysis identifies “smart manufacturing,” “digital twin,” and “industry 4.0” as core themes, with increasing emphasis on artificial intelligence, optimization, collaborative robots, and Industry 5.0 in recent years. Co-authorship and country collaboration maps illustrate dense scholarly networks centered around prominent authors and regions. Despite significant progress, the study identifies gaps in real-world implementation, standardization, and ethical considerations. These insights offer valuable direction for future interdisciplinary research and policy strategies aimed at integrating DT technologies into next-generation manufacturing ecosystems.

Shift Left Security

Gaurav Malik, Prashasti Prashasti — Wed, 30 Apr 2025 00:00:00 +0000

Shift Left Security is a proactive approach to software development that aims to integrate security measures at the beginning of the software development lifecycle (SDLC) and at the design and development phases. In the past, security for software development has been reactive, looking for vulnerabilities at the test or deployment stages. However, this method has proven ineffective in the face of the complexity of these modern software systems and the frequency of cyber-attacks. Shift Left Security instead highlights embedding security practices from the beginning to capture vulnerabilities and detect and remediate them at the very beginning before they even hit the production stage. Continuous security testing, early risk assessment, and real-time feedback loops to rectify vulnerabilities immediately, given that solving them is critical during development, are a part of the proactive model. Shift Left Security integrates security into the SDLC, ensuring the security posture of the software applications is strengthened, post-release remediation is reduced, and time to market is accelerated. The significant advantage of it is that it lets organizations open up to a conversation around security as early as possible without the risk of it becoming an issue. Shift Left Security is an area of interest that this study explores in terms of its principles, benefits, challenges, and tools. This serves as a valuable offering that offers a hands-on approach to adopting this approach by organizations to achieve more secure, resilient software products through improved development efficiency and better protection against emerging cyber threats.

The Role of GitLab Runners in CI/CD Pipelines: Configuring EC2, Docker, and Kubernetes Build Environments

Naga Murali Krishna Koneru — Wed, 30 Apr 2025 00:00:00 +0000

This research studies GitLab Runners optimization in CI/CD pipelines across the EC2, Docker, and Kubernetes environment configurations. It shows that such key strategies for enhancing build performance and resource utilization would reduce build time by 65 percent and resource costs by 40 percent. Practical recommendations for configuring runners to achieve optimal efficiency are presented in the context of analyzing 200 enterprise pipelines. The key optimization techniques are autoscaling based on real-time metrics, advanced caching to minimize the rebuilds, and tuning the resource allocation to avoid over-provision. The study further looks into the capability of machine learning models to optimize the number of runners dynamically, predict the hit or not on the cache, and automatically pick up the execution environment. When these innovations are applied, CI / CD pipeline performance will improve by reducing idle resources, building time, and optimizing resource utilization. The paper shows that experts can achieve very good availability and cost efficiency by adapting the configuration of GitLab Runners. The research also discusses the evolution of automated environment selection and machine learning-based performance tuning. This framework serves as the base for organizations to increase their CI/CD pipeline development rate and facilitates a faster, more reliable, and cheaper software delivery.

Brain-Computer Interfaces in Assistive Technologies: A Bibliometric Review

Loso Judijanto, Arnes Yuli Vandika, Ramli Toalib — Wed, 30 Apr 2025 00:00:00 +0000

This study presents a comprehensive bibliometric review of scholarly literature on Brain-Computer Interfaces (BCIs) in assistive technologies, aiming to map research trends, intellectual structure, and collaborative patterns from 2000 to 2024. Using data retrieved from the Scopus database and analyzed through VOSviewer, this review identifies key contributors, institutional affiliations, and country-level collaborations. Results show a steady increase in publication output, with a sharp surge in 2024, indicating growing academic and clinical interest in BCI-assisted systems. The United States, Germany, and India emerge as the most productive countries, while institutions such as Eberhard Karls Universität Tübingen and Harvard Medical School lead in scholarly output. Author co-authorship analysis reveals influential figures and collaborative hubs, particularly in Europe and North America. Thematic clustering of keywords uncovers major research domains, including neurophysiological signal processing, machine learning applications, robotic control systems, and user-focused communication aids. Overlay and density visualizations suggest an evolution from foundational EEG-based research to more sophisticated, AI-enhanced and ethically grounded assistive technologies. This review provides a data-driven understanding of the field’s development and highlights future directions toward more inclusive, adaptive, and scalable BCI solutions for individuals with disabilities.

Optimizing CI/CD Pipelines for Multi-Cloud Environments: Strategies for AWS and Azure Integration

Naga Murali Krishna Koneru — Wed, 30 Apr 2025 00:00:00 +0000

CI/CD pipelines are essential for modern software development to speed up application delivery and ensure reliability. However, organizations experience considerable management difficulties when they operate Continuous Integration and Deployment workflows between AWS and Azure. The research discusses multiple approaches to optimizing CI/CD pipelines and demonstrates their integration between AWS and Azure systems. The complete implementation guidelines within the method include selecting tools and best practices along with the necessary architectural elements to construct secure, scalable, and successful CI/CD pipelines. Success in deployment requires using standardized CI/CD platforms, infrastructure code implementation, and security platforms spanning multiple cloud environments, price reduction technologies, and consolidated monitoring tools. The deployment process framework integrates cloud platforms by implementing a solution that merges interoperability and security management alongside cost control functions. This proposal demonstrates its worth by applying the example project to prove significant benefits: fast deployment speed, lower costs, and dependable system infrastructure. The document explores actual applications of optimized pipelines, which decrease operational complexity and enhance resource utilization efficiency. The report includes deployment guidelines supplemented by practical examples to guide organizations during their adoption phase. Standardized CI/CD management approaches allow organizations to simplify deployment pipelines and automate workflow processes during multi-cloud connectivity risk management. The surveyed findings regarding optimizing AWS and Azure CI/CD workflows enable organizations to improve their DevOps performance in complex cloud infrastructure.

Bridging DFM and Sourcing Strategy

Ganpati Goel, Rahul Brahmbhatt, Mukesh Reddy Dhanagari — Wed, 30 Apr 2025 00:00:00 +0000

This paper focuses on aligning Design for Manufacturing (DFM) principles with sourcing strategies to minimize one important step in rotor shaft manufacturing. The rotor shaft, an important part in high stress mechanical systems such as turbines or motors, is required to abstract and has to be designed and manufactured according to strict rules. Implementing DFM in the earlier phases of the design stream enables the production to be streamlined through geometric simplification, material selection, and process optimization. Early collaboration between design engineers and suppliers is necessary to integrate manufacturing capabilities into the required design to avoid the cost and delay of redesigns. The study aims to examine if the sourcing strategies affect DFM implementation. Technological strengths are used to select the suppliers so that materials and manufacturing processes match design specifications. Advanced machining techniques, additive manufacturing, and automation are key enablers of the DFM principle, minimizing the cost of production and lead time. The Supplier A and B case studies show how suppliers use rotor shaft production practices, advanced manufacture, and agile production for production efficiency. The study concludes that the success of DFM and sourcing strategies for rotor shaft manufacturing will need to be successful for rotor shaft manufacturing to be efficient, reduce costs, and maintain high-quality standards. Manufacturers are encouraged to work closely with suppliers, adopt flexible sourcing strategies, and invest in technology to remain competitive. The combination of the DFM principle and the latest strategies used for sourcing enables manufacturers to optimize production processes, reduce waste, improve the quality and performance of the product, and have a solid, economical supply chain.

The Impact of Explainable AI on User Trust and Ethical AI Adoption in Indonesia

Loso Judijanto, Sayed Achmady, St. Annisa Nurul Fadhilah, Arnes Yuli Vandika — Wed, 30 Apr 2025 00:00:00 +0000

Explainable Artificial Intelligence (XAI) is increasingly recognized as a critical enabler of trust and ethical AI adoption. This study explores the impact of XAI on user trust and ethical AI adoption within Indonesian academia through a qualitative analysis of five informants, including AI researchers, university administrators, and policymakers. The findings reveal that XAI enhances transparency and ethical awareness while fostering trust among academic stakeholders. However, technical complexity, resource limitations, and resistance to change pose significant barriers to implementation. The study also identifies opportunities for fostering XAI adoption, such as collaborative initiatives, government support, and tailored training programs. These insights contribute to the growing discourse on leveraging XAI for promoting ethical and trustworthy AI practices in academia.

The Integration of AI and IoT in Cyber-Physical Systems for Smart Manufacturing in Indonesia

Wed, 30 Apr 2025 00:00:00 +0000

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) within Cyber-Physical Systems (CPS) represents a transformative opportunity for smart manufacturing in Indonesia. This study employs a qualitative approach, with insights from five informants, to explore the current state, challenges, opportunities, and strategic recommendations for CPS adoption. Findings reveal that while CPS adoption is in its early stages, it has significant potential to enhance operational efficiency, sustainability, and global competitiveness. Challenges such as limited infrastructure, skill gaps, and cybersecurity concerns are prevalent. However, opportunities exist in leveraging government initiatives, fostering local innovation, and promoting sustainability. Strategic recommendations include enhancing digital infrastructure, providing financial incentives, upskilling the workforce, and establishing clear regulatory frameworks. These insights contribute to advancing Indonesia's Industry 4.0 agenda and offer a roadmap for stakeholders to navigate CPS integration effectively.

Hyperloop Trial Run: Budgeting Under Uncertainty

Ganpati Goel, Rahul Brahmbhatt — Wed, 30 Apr 2025 00:00:00 +0000

The Hyperloop project is a radical new approach to high-speed transportation that could drastically reduce travel times between major cities with little environmental impact. As the project progresses, the trial runs, and promotional events must successfully convince the investor that the technology is feasible to execute the trial runs and promotional events. There are uncertain facts about the technology, the market conditions, and the regulatory difficulties in budgeting for such a complex and innovative System. The challenge for Virgin Hyperloop is to make it through this safely, with careful cost estimation, risk management, and developing a strategic plan. Public-private partnerships, cardio-logical innovations, and scenario-based budgeting are key strategies for sustaining and scaling up. Risk is managed, and the context evolves to meet that need but with long-term funding of project growth in place. Through examining a trial run of Virgin Hyperloop’s promotional scenario and how the financial strategies of Virgin Hyperloop may affect the budgeting process and will be friendly to scale this innovative transportation system, the paper unravels. The other effect of following Hyperloop technology in the world transportation industry became visible.

Evaluating AI Responses: A Step-by-Step Approach for Test Automation

Sooraj Ramachandran — Wed, 30 Apr 2025 00:00:00 +0000

Artificial Intelligence (AI) applications are transforming business operations, yet ensuring the accuracy, relevance, and reliability of AI-generated responses remains a critical challenge. This paper explores various methodologies for AI response evaluation, progressing from basic string comparisons to machine learning (ML)-based assessments and advanced Retrieval-Augmented Generation (RAG) techniques. We examine the advantages and limitations of each approach, illustrating their applicability with C# implementations. Our findings suggest that while traditional methods like fuzzy matching provide quick validation, ML-based and RAG-based approaches offer superior contextual understanding and accuracy. The study highlights the importance of automated evaluation pipelines for AI systems and discusses future research directions in improving AI response testing methodologies.

Smart, Safe, and Strategic: Transforming HR Data into Actionable Insights Without Compromising Security

Kuwarpreet Singh — Wed, 30 Apr 2025 00:00:00 +0000

In today’s digital age, healthcare organizations are starting to use Human Resource (HR) data to make informed workforce decisions, enhance staffing, promote employee well-being, and navigate the ever-evolving regulatory landscape. This shift is largely due to well-built analytics tools found in platforms like Workday, which provide HR leaders with real-time insights into key metrics such as turnover rates, performance trends, and skills gaps. However, with all these benefits come serious risks. HR data in healthcare often includes very private information like health records, salaries, and personal details. If this information falls into the wrong hands, it can cause legal problems, damage the organization’s reputation, and hurt employees. Because of this, it's not enough to just use data well—it must also be protected at every stage. This paper explores how healthcare institutions can effectively and safely transform HR data into actionable insights through advanced analytics, all while prioritizing data privacy and compliance. It examines modern encryption techniques, privacy-preserving machine learning, and data governance frameworks that empower HR teams to achieve better outcomes securely. By reviewing case studies, peer-reviewed research, and industry best practices, this paper sheds light on the challenges, solutions, and emerging trends that will define the future of secure, data-driven HR ecosystems in healthcare.