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INDONESIA
International Journal of Research and Applied Technology (INJURATECH)
Published by Universitas Komputer Indonesia
ISSN : 28100662     EISSN : 28077229     DOI : https://doi.org/10.34010/injuratech.v1i2.5902
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
INJURATECH cover all topics under the fields of Computer Science, Information system, and Applied Technology. Scope: Computer Based Education Information System Database Systems E-commerce and E-governance Data mining Decision Support System Management Information System Social Media Analytic Data visualization Cloud computing platforms Distributed file systems and databases Big data technologies Data capture and storage Computer Architecture and Embedded Systems Geographic information system (GIS) Remote Sensing Software Engineering Internet and Web Applications Mobile Computing Hardware and physical security Mobile Computing Security management and policies Block chain Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 251 Documents
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How Consumer Engagement is Fuelling the Next Wave of Global E-Marketplace Growth Danisa, Salma
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

The transformation of the global electronic commerce landscape is currently shifting from a purely transactional model toward an ecosystem centered on consumer engagement. This research aims to analyze how digital engagement mechanisms—such as social features, gamification, and AI-driven personalization—have become the primary drivers of e-marketplace growth in the global market. Through a systematic literature review, this study finds that two-way interactions between platforms and users not only enhance retention but also significantly reduce customer acquisition costs. The findings indicate that "engagement" is no longer merely a supplementary element but a core growth engine defining the new wave of e-commerce. This article offers a theoretical contribution in the form of an Engagement-Driven Growth framework and provides practical recommendations for platform managers to prioritize experiential value over mere transaction volume.
Integration of GIS and Remote Sensing for Environmental Monitoring: A Systematic Literature Review Hasan, Mochammad Fuad
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

The integration of Geographic Information Systems (GIS) and Remote Sensing (RS) has become essential for addressing escalating global environmental challenges. This systematic literature review, strictly adhering to PRISMA guidelines, synthesizes 115 peer-reviewed articles published between 2016 and 2026 to evaluate the current state, methodological trends, and technological synergies in environmental monitoring. Our findings reveal that Forestry and Land Use/Land Cover (LULC) change, alongside water resource and disaster management, are the predominant application domains. Crucially, the review highlights a significant paradigm shift from traditional analytical methods to advanced multi-sensor data fusion and the rapid incorporation of Artificial Intelligence (AI) and Machine Learning algorithms, which drastically enhance spatial predictive accuracy. Despite these advancements, challenges such as massive geospatial data handling and sensor interoperability remain prevalent. Ultimately, this study provides a comprehensive framework for researchers and policymakers, emphasizing that leveraging cloud computing and AI-driven GIS-RS synergies is vital for formulating robust, data-driven environmental conservation and disaster mitigation strategies.
Data-Driven Urban Planning: The Role of Spatial Data Mining in Smart City Decision Support Systems Rafdhi, Agis Abhi
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

This study aims to address the analytical limitations of traditional Geographic Information Systems (GIS) in urban governance by proposing an integrated, cloud-based Spatial Data Mining Decision Support System (SDM-DSS) framework. Employing a systematic literature review methodology, recent peer-reviewed studies (2021–2026) from major scientific databases were extracted, compared, and thematically synthesized to identify architectural vulnerabilities in current smart city models. The results indicate that while advanced SDM algorithms exhibit high theoretical accuracy for modeling urban phenomena, their practical deployment is frequently hindered by fragmented architectures, localized computational bottlenecks, and a lack of real-time Internet of Things (IoT) integration. To resolve these operational deficiencies, this study formulates a three-layered conceptual architecture comprising a Data Management Layer, a Spatial Analytics Engine, and a Presentation Dashboard. By decoupling heavy computational workloads into a scalable cloud environment, the proposed framework seamlessly translates complex algorithmic outputs into intuitive, actionable policy directives, as demonstrated through dynamic public facility allocation and predictive disaster mitigation scenarios. In conclusion, the integrated SDM-DSS architecture fundamentally transforms reactive urban planning into a proactive, predictive paradigm. Future research should prioritize the empirical prototyping of this framework using real-time municipal data streams and the incorporation of privacy-preserving machine learning techniques to ensure data sovereignty.
Blockchain Integration in Academic Credentialing: Implications for Employment Markets and Economic Growth Fahrezi, Muhamad
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

The integration of blockchain technology in academic credentialing has the potential to revolutionize traditional systems of verifying educational achievements. The study aims to assess how blockchain can improve the efficiency, transparency, and security of academic qualification verification processes. Using a qualitative research approach, we analyzed current trends and reviewed of blockchain applications in education. The results indicate that blockchain technology significantly reduces credential fraud, enhances labor market mobility, and accelerates hiring processes. Furthermore, blockchain’s decentralized nature allows for greater control and ownership of academic credentials, making them more accessible and verifiable across borders. These findings highlight the transformative potential of blockchain to create a more efficient, equitable, and globalized labor market. As a result, blockchain could not only streamline employment practices but also drive economic growth by improving human capital utilization and fostering global talent mobility
Algorithmic Bias in Political Content Curation on the Twitter/X Platform: A Machine Learning Perspective Danisa, Salma
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

This study explores the mechanisms of algorithmic bias in the curation of political content on the Twitter/X platform through the lens of Machine Learning (ML). Amidst increasing global polarization, recommendation algorithms are frequently accused of facilitating the creation of echo chambers. This paper highlights how the objective functions of ML models, specifically the maximization of user engagement, inadvertently amplify extremist and partisan content. Utilizing a systematic literature review, the research identifies that bias originates not only from training data (data bias) but also from architectural reinforcement mechanisms (reinforcement bias). The findings suggest that the interaction between user behavior and algorithmic feedback loops creates a self-perpetuating cycle of polarization. This study contributes a technical mapping of how collaborative filtering and deep learning algorithms contribute to the fragmentation of the digital public sphere. The results are intended to serve as a foundational framework for developers and regulators in designing curation systems that are more transparent and politically neutral.
Access Control and Security Monitoring in Blockchain-Based Cloud Information Systems: A Systematic Review Hayati, Euis Neni
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

The integration of blockchain technology into cloud information systems has emerged as a promising approach to enhance data transparency, trust, and security. However, the transition toward a decentralized and distributed architecture introduces novel challenges, particularly concerning the enforcement of access control and security monitoring mechanisms within cloud environments. This study presents a systematic literature review focusing specifically on the access control models and security monitoring strategies implemented in blockchain-based cloud systems. Based on the analysis of recent peer-reviewed studies filtered through a structured methodology, the findings indicate that smart contract-driven Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Capability-Based Access Control (CapBAC) are the dominant approaches for autonomous authorization management. In terms of security monitoring, blockchain-enabled audit logs have proven to provide a high degree of traceability and absolute tamper resistance, effectively mitigating malicious insider threats. Despite these significant advantages, this review identifies that scalability issues, high network latency, and computational costs remain critical bottlenecks for industrial-scale adoption. Consequently, this review highlights current research gaps and recommends future research directions, including the implementation of off-chain scaling solutions, Zero-Knowledge Proofs (ZKPs) for enhanced privacy.
Artificial Intelligence Integration and the Reconfiguration of Organizational Control Systems A Critical Literature Review of Knowledge Governance Structural Transformation and Strategic Renewal Fahrezi, Muhammad
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

The integration of Artificial Intelligence has profoundly challenged the foundations of organizational control systems traditionally based on hierarchical authority, formal rules, and managerial supervision. While existing studies largely emphasize the operational benefits of Artificial Intelligence, limited attention has been given to its implications for control structures, knowledge governance, and strategic renewal. This study aims to critically examine how Artificial Intelligence reconfigures organizational control systems through an integrative literature review. Adopting a critical and interpretive synthesis of interdisciplinary scholarship, this paper explores the transformation of control logic, the mediating role of knowledge governance, and the structural conditions that enable strategic renewal. The findings suggest that Artificial Intelligence shifts organizational control from compliance-oriented supervision toward adaptive, algorithmically mediated coordination embedded in digital infrastructures. This reconfiguration redistributes control authority, reshapes organizational structures, and enables continuous strategic sensemaking. The study contributes by offering a holistic conceptual framework that positions Artificial Intelligence as a catalyst for a new paradigm of organizational control rather than a mere technological enhancement.
Data Driven Organizational Culture and Managerial Cognition Reshaping Decision Authority Capability Development and Governance Structures in Algorithmic Environments Fahrezi, Muhamad
International Journal of Research and Applied Technology (INJURATECH) Vol. 6 No. 1 (2026): June 2026 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

The rapid expansion of artificial intelligence, big data analytics, and algorithmic decision systems has transformed organizational structures and decision processes. This study analyzes how data-driven organizational culture and managerial cognition jointly reshape decision authority, capability development, and governance structures in algorithmic environments. Using a narrative literature review, the research integrates perspectives from strategic management, organizational theory, information systems, and digital governance to develop a comprehensive conceptual framework. The findings show that a data-driven culture promotes evidence-based norms that encourage managerial cognitive adaptation toward probabilistic reasoning and analytics literacy. This cognitive shift legitimizes decentralized yet algorithmically bounded decision authority characterized by shared human–machine accountability. Structural transformation further requires hybrid capabilities combining data literacy, cross-functional integration, and governance competence. Governance redesign institutionalizes oversight mechanisms that reinforce cultural alignment through continuous learning. The study advances understanding of the socio-cognitive foundations of algorithmic transformation and provides strategic guidance for building adaptive, accountable organizations in data-intensive environments.
Cloud Computing in Advancing Intelligence Capabilities for Indonesia National Security, Literature Review Study Meliala, Brandon Nathanael
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

Cloud computing has emerged as a transformative technology that enhances intelligence capabilities and supports national security operations in the digital era. This study aims to examine the role of cloud computing in strengthening Indonesia’s intelligence infrastructure through a literature review approach. The research analyses existing academic studies, policy reports, and technological frameworks related to cloud-based systems in intelligence management, data processing, and information sharing. The findings indicate that cloud computing enables faster data integration, scalable storage, real-time analytics, and improved inter-agency collaboration, which are essential for effective intelligence gathering and decision-making. However, the adoption of cloud technology in national security also raises challenges, including cybersecurity risks, data sovereignty concerns, and regulatory limitations. The study concludes that strategic implementation of secure cloud architectures, supported by robust policies and institutional capacity, can significantly enhance Indonesia’s intelligence capabilities while maintaining data protection and national security resilience.
Data Mining and Corporate Financial Distress Prediction: Integrating Classical Bankruptcy Models with Contemporary Machine Learning Approaches Fahrezi, Muhamad
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

Corporate financial distress prediction has shifted from classical ratio based statistical models toward data driven machine learning systems, raising concerns regarding the trade-off between predictive accuracy and interpretability. This study evaluates the integration of classical bankruptcy models with contemporary machine learning approaches to develop a robust and transparent early warning framework. Using a Literature Review, peer reviewed studies indexed in Scopus, Web of Science, and IEEE Xplore were synthesized, focusing on comparisons between the Z score model developed by Edward Altman, logistic regression, and modern algorithms such as support vector machines, ensemble learning, and neural networks. The findings indicate that machine learning models, particularly ensemble methods, demonstrate superior predictive capability in capturing nonlinear financial relationships. However, traditional accounting indicators remain fundamental predictors of distress. The study concludes that a hybrid framework integrating accounting based theory with machine learning optimization offers the most effective and strategically sustainable approach to corporate risk assessment.

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