The Eastasouth Journal of Information System and Computer Science

Artificial Intelligence in Precision Medicine: Enhancing Chronic Disease Management and Genomic Drug Discovery through Predictive Modeling

2025-06-12T14:50:47+00:00

This paper explores the transformative role of Explainable Artificial Intelligence (XAI) in precision medicine, focusing on its application in chronic disease management and genomic drug discovery. Through two detailed workflow diagrams, the study highlights the integration of XAI within the clinical decision-making pipeline and biomedical research domains. Figure 1 illustrates a comprehensive process encompassing data acquisition, preprocessing, predictive modeling, and clinician feedback, all underpinned by XAI techniques such as SHAP, LIME, and attention mechanisms. This workflow enhances trust and transparency in AI-driven predictions, empowering clinicians to interpret and act on machine-generated insights. Figure 2 extends this understanding by mapping XAI applications to chronic disease monitoring and genomic analysis. In chronic care, XAI enables risk stratification and personalized interventions, while in genomic drug discovery, it facilitates the identification of potential targets through interpretable machine learning models. Together, these figures underscore XAI’s critical role in translating complex data into actionable healthcare outcomes. By promoting accountability, user trust, and informed decision-making, XAI emerges as a cornerstone for the ethical and effective deployment of artificial intelligence in precision medicine. The paper concludes that integrating explainability into AI models is not only a technical necessity but also a fundamental step toward safer, smarter, and more inclusive healthcare systems.

Accelerating Drug Discovery: The Role of Generative AI and Big Data Analytics

2025-06-18T07:35:16+00:00

Drug discovery has long been characterized by extensive timelines, high costs, and significant risks, often taking more than a decade and billions of dollars to bring a single drug to market. However, the convergence of generative artificial intelligence (AI) and big data analytics is fundamentally reshaping this landscape. This paper provides an in-depth analysis of generative AI especially models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures combined with vast biological and chemical datasets, is transforming molecular design, target identification, and compound optimization. Through a systematic review of literature, comparative model evaluation, and real-world case studies including AlphaFold, the paper explores the efficacy of these technologies in accelerating drug discovery. A hybrid methodology combining data mining, model testing, and bioinformatics simulation is employed. The results demonstrate significant improvements in candidate molecule generation, predictive modeling accuracy, and time-to-market for new drugs. Future challenges such as data interoperability, ethical considerations, and regulatory compliance are also discussed. The study concludes by highlighting the immense potential of AI and big data in ushering a new era of precision medicine and personalized therapeutics.

Analysis of Upstream Cloud Technology Acceptance at Pertamina Hulu Energi Regional 4

2025-08-07T16:29:31+00:00

This study aims to analyze the factors influencing the acceptance of Upstream Cloud technology at PT Pertamina Hulu Energi Regional 4. Upstream Cloud is part of the company's digital transformation strategy, expected to improve operational efficiency and competitiveness in the oil and gas industry. Using the Technology Acceptance Model (TAM) approach, this research examines the influence of Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Confidence Level of Users (CL), and Cost Effectiveness (CE) on the level of technology acceptance within the organization. The research was conducted through a survey involving employees of PT Pertamina Hulu Energi Regional 4, with data analyzed using inferential statistical methods to determine the relationships and the magnitude of influence of each variable. The results indicate that PU has a positive and significant effect of 33.3%, PEOU of 9.2%, CL of 39.1%, and CE of 14.9% on technology acceptance. Simultaneously, these four variables contribute a total of 96.5% to the acceptance of Upstream Cloud technology. These findings have important strategic implications for the company, including the need to enhance user competence and confidence through technical training and mentoring, intensify communication regarding the tangible benefits of cloud technology, and strengthen technical support. Additionally, although PEOU has a relatively small influence, ensuring ease of use remains essential to minimize adoption barriers, particularly for non-technical users. Regular evaluations of the technology's effectiveness are also recommended to adapt to the dynamic operational needs and technological advancements in the oil and gas industry.

Using Image Similarity Metrics to Discriminate Between DALL-E Generated Art and Original Art

2025-08-11T04:05:16+00:00

The rise of image-generating artificial intelligence (AI) tools like OpenAI’s DALL-E has changed the way art is made. It brings up important questions about originality, authorship, and ethical implications. I explore the originality of AI generated art through a quantitative similarity analysis using Bhattacharyya distance and Euclidean distance to measure color and structural similarity between AI outputs and their reference artworks. I analyze three distinct prompt variations—one including the artist’s name, another with a detailed description but no artist reference, and a third requesting a reinterpretation rather than replication of an original painting, namely the Mona Lisa. I find that AI generated images exhibit varying degrees of similarity depending on prompt specificity. The metrics found that mentioning the artist’s name in the prompt resulted in more similar outputs than when asked for a direct reinterpretation. Similarity metrics indicate that AI generated outputs tend to resemble each other more closely than they do the original painting, implying that AI models operate based on learned visual patterns rather than direct replication. The study emphasizes how important it is to have explicit ethical guidelines and legal frameworks to be able to regulate AI’s influence in the artistic domain.

Implementation Strategy of the SOLISPARES Model for Developing Excellent Character and High Competence in Indonesian Air Force Personnel

2025-08-28T01:40:36+00:00

This study aims to examine the implementation strategy of the SOLISPARES Model in developing human resources of the Indonesian Air Force (IAF) to foster excellent character and high competence among personnel. The SOLISPARES Model integrates five core dimensions: Personal Excellence, Interpersonal Capital, Organizational Excellence, Communal Collaboration, and Everlasting Glory. Each dimension is embedded with essential values—such as honesty, trustworthiness, discipline, loyalty, justice, and continuous learning—that collectively build a holistic framework for character and competence development. Employing a qualitative research method with a case study approach, this research was conducted within IAF educational and training institutions. Data were gathered through in-depth interviews with key stakeholders, participatory observation, and analysis of official strategic documents. Findings reveal that successful implementation of the SOLISPARES Model is determined by visionary leadership, strong organizational policy support, structured mentoring, and a continuous evaluation system. The application of the model significantly improved personnel’s integrity, discipline, teamwork, and technical expertise. The study recommends institutionalizing the SOLISPARES Model into the IAF’s human resource development curriculum as a long-term strategy to enhance institutional competitiveness, operational readiness, and resilience in the face of modern defense challenges.

Analysis of the Trends in AI-as-a-Service (AIaaS) Implementation in Technology Companies in Indonesia

2025-08-28T04:55:01+00:00

This study examines the impact of adoption drivers, perceived benefits, and implementation challenges of Artificial Intelligence-as-a-Service (AIaaS) on the organizational performance of technology companies in Indonesia. Using a quantitative approach, data were collected from 200 respondents through structured questionnaires and analyzed with multiple regression techniques. The results reveal that adoption drivers and perceived benefits have a significant positive influence on organizational performance, while implementation challenges exert a negative impact. These findings suggest that while AIaaS offers considerable potential for efficiency, innovation, and competitive advantage, unresolved barriers such as data security, system integration, and limited human resources hinder its effectiveness. This study contributes to the growing body of literature on AI adoption in emerging economies by highlighting the dual role of enablers and inhibitors in shaping AIaaS outcomes. Practical implications include the need for organizations to invest in digital skills development and risk management, and for policymakers to establish clear regulatory frameworks to accelerate responsible AIaaS adoption in Indonesia.

The Role of Federated Learning in Enhancing Data Privacy in Distributed Environments in Indonesia

2025-08-28T01:40:31+00:00

This study investigates the role of federated learning (FL) in enhancing data privacy within distributed environments in Indonesia. With the increasing reliance on digital technologies, data privacy has become a critical concern, particularly in decentralized systems. A quantitative research approach was employed, involving 130 respondents from various professional backgrounds engaged with distributed data systems. Data were collected using a structured questionnaire with a five-point Likert scale (1–5) and analyzed using SPSS version 25. Descriptive, correlation, and regression analyses revealed that federated learning significantly contributes to improving confidentiality, trust, and compliance in distributed environments. The results indicate that FL not only safeguards sensitive data but also enhances stakeholder confidence and supports adherence to regulatory standards, such as Indonesia’s UU PDP. The study provides empirical evidence supporting the adoption of federated learning as a privacy-preserving technology and offers practical insights for organizations and policymakers seeking secure and compliant digital ecosystems.

Research Trends in Cyber-Physical Systems within Information Systems: A Bibliometric Approach

2025-08-28T01:40:45+00:00

This study presents a comprehensive bibliometric analysis of research trends in Cyber-Physical Systems (CPS) within the Information Systems (IS) domain, utilizing data from the Scopus database (2010–2025) and analyzed using VOSviewer. By examining keyword co-occurrence, overlay mapping, density visualization, and collaboration networks, the study identifies the thematic structure, intellectual linkages, and global research collaborations shaping the CPS–IS landscape. Results reveal that CPS research is highly interdisciplinary, with central themes including internet of things, network security, machine learning, and information management, while emerging areas such as digital twin, intrusion detection, and false data injection attacks are gaining momentum. The overlay visualization indicates a recent shift toward AI-driven security and optimization in CPS applications. Authorship and country collaboration analyses highlight China, the United States, Germany, and India as leading contributors with extensive international linkages. This study contributes to the theoretical understanding of CPS as an integrated socio-technical construct in IS and provides practical insights for aligning research, policy, and industrial strategies with evolving technological priorities in the era of Industry 4.0.

Research Trends in Smart Workflow Automation: A Bibliometric Study from Scopus

2025-08-28T01:40:39+00:00

This study presents a comprehensive bibliometric analysis of smart workflow automation research using Scopus-indexed publications to map its intellectual structure, thematic evolution, and global collaboration patterns. A dataset covering the period 2000–2025 was extracted, cleaned, and analyzed using VOSviewer for science mapping and Microsoft Excel for performance metrics. Results reveal that the field is anchored by core concepts such as automation, internet of things, machine learning, and work-flows, which consistently occupy central positions in the research network. Emerging themes, including blockchain, supply chains, security, and ubiquitous computing, reflect a shift toward secure, interconnected, and efficiency-driven automation ecosystems. Co-authorship and country collaboration analyses highlight the pivotal roles of the United States, China, and Germany, supported by extensive cross-border partnerships. Temporal trend mapping demonstrates a transition from early industrial automation and foundational cyber-physical infrastructure toward integrated, value-oriented applications. The study offers practical guidance for industry adoption strategies, enriches theoretical understanding of the field’s conceptual landscape, and identifies promising research frontiers for future exploration.

Bibliometric Mapping of Extended Reality (XR) in Education and Business Domains

2025-08-28T01:40:50+00:00

This study presents a comprehensive bibliometric mapping of Extended Reality (XR) research in education and business domains, providing insights into thematic trends, collaborative networks, and emerging areas of interest. Using the Scopus database as the primary data source, publications from 2010 to 2025 were retrieved and analyzed exclusively with VOSviewer to generate keyword co-occurrence, author collaboration, country collaboration, temporal evolution, and density visualizations. The results reveal that virtual reality, augmented reality, extended reality, and engineering education are the most prominent and interconnected themes, with newer research increasingly focusing on metaverse, artificial intelligence, and Industry 4.0 integration. Co-authorship and country network maps show that the United States, Italy, Germany, and India are central hubs driving global research partnerships, while author collaborations cluster into specialized yet interconnected groups. The study highlights how education-oriented XR research emphasizes immersive learning, e-learning, and gamification, while business-focused studies target marketing innovation, customer engagement, and sustainability. These findings contribute to a holistic understanding of XR’s interdisciplinary evolution, offering strategic guidance for researchers, educators, industry leaders, and policymakers to foster innovation and cross-domain integration.

PT. Spektrum Kreasi Pratama's Marketing Communication Strategy in Building Value Creation Through Personal Selling (Case Study of Dr. Ainun Habibie Regional Hospital, Parepare City)

2025-08-28T01:41:03+00:00

In an era of increasingly fierce business competition, companies must be able to design effective marketing communication strategies, especially in the service sector, such as healthcare services. One approach that is still relevant and effective is personal selling, which allows companies to build long-term relationships with clients through direct communication. This study examines PT. Spektrum Kreasi Pratama's marketing communication strategy in creating value through personal selling, with a focus on Dr. Ainun Habibie Regional Hospital in Parepare City. This study aims to analyze the influence of personal selling strategies in creating value creation, identify the challenges faced in its implementation, and explore how this strategy influences the hospital's decision to choose PT. Spektrum Kreasi Pratama as a partner for medical equipment calibration services. This study uses a descriptive approach with data collection techniques through in-depth interviews, participatory observation, and documentation. The results of the study indicate that personal selling plays an important role in building stronger relationships, increasing trust, and strengthening customer loyalty. However, communication challenges and market competition are obstacles that need to be overcome. This study concludes that personal selling is key to creating sustainable value creation and facilitating a successful partnership between PT. Spektrum Kreasi Pratama and healthcare institutions.

Social Media Analysis of Kang Dedi Mulyadi's Instagram Account In Managing Digital Advertising And Persona Branding Using The Peso Model

2025-08-28T01:40:59+00:00

This study aims to analyze the digital advertising management strategy and personal branding of Kang Dedi Mulyadi (KDM) through his Instagram account, using the PESO (Paid, Earned, Shared, Owned) model. This topic is important because it demonstrates the dynamics of digital political communication in the era of increasingly personal and culture-based social media. The research method used is a qualitative approach with descriptive analysis of KDM's Instagram content. The results show that KDM succeeded in building authentic digital communication through emotional narratives, strong visuals, and the use of local language and a Sundanese Buhun cultural approach. This strategy does not rely on paid promotions, but relies on the organic power of humanistic and socially relevant content. In addition, this study recommends the development of the PESO model by adding Cultural Capital Media elements to adapt to the context of Indonesian political communication which is rich in local cultural values. In conclusion, KDM's success demonstrates the importance of culture-based content and emotional closeness in strengthening the effectiveness of digital political communication on social media.

SINERGISTA (Agrotourism Synergy): A Sustainable Tourism Development Strategy Based on Digitalization Through the Pentahelix Collaboration Model to Support the 2030 SDGs

2025-08-28T01:40:55+00:00

Sustainable tourism is a key pillar in achieving the Sustainable Development Goals (SDGs) 2030. This study introduces SINERGISTA (Sinergi Agrowisata) as a digital-based sustainable tourism development strategy through a Pentahelix Connectors collaboration model involving government, academia, business actors, communities, and media. The objective of this research is to formulate an implementable strategy for digital-based agrotourism development aimed at enhancing economic, social, environmental, and cultural value in a sustainable manner. This study employs a descriptive-exploratory approach through in-depth interviews, field observations, and documentation studies on leading agrotourism destinations in Indonesia. Findings reveal that integrating digitalization into marketing platforms, reservation systems, and virtual reality promotions expands market reach, increases tourist engagement, and facilitates transparent destination management. Moreover, the Pentahelix Connectors collaboration is proven effective in reinforcing synergy among stakeholders in promoting service innovation, improving human resource capacity, and fostering participatory environmental management. Strategic recommendations include strengthening digital literacy among agrotourism actors, ensuring regional government policy support, and providing inclusive and adaptive technological infrastructure.