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ADI Journal on Recent Innovation (AJRI)
Published by Pandawan Sejahtera Indonesia
ISSN : 26859106     EISSN : 26860384     DOI : doi.org/10.34306/ajri
Core Subject : Religion, Science, Education, Engineering,
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Industrial & Manufacturing Engineering
AJRI is a reputable Scientific Publication Media aim to foster research finding that concentrate towards recent innovation and creativity to support advancement in global civilization and humanity. AJRI Journal published two times a year (March & September) by Asosiasi Dosen Indonesia (ADI) Publisher. AJRI Journal invites all manuscripts on Multidisciplinary topics.
Arjuna Subject : Umum - Umum
Articles 10 Documents
 1 
Search results for , issue "Vol. 7 No. 2 (2026): March" : 10 Documents clear
A Converged Blockchain and Artificial Intelligence Approach for Strengthening Transparency and Trust in Digital Enterprises Novianti, Leoni; Azizah, Nur; Mardiana, Mardiana; Perez, Carlos
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1374

Abstract

Digital business ecosystems increasingly rely on advanced technologies to ensure transparency, security, and trust in operational processes. However, traditional centralized systems still struggle with issues such as data manipulation, lack of traceability, and weak auditability, which affect stakeholder confidence and operational efficiency. This study investigates the integration of Blockchain and Artificial Intelligence (AI) as a combined framework to enhance transparency and trust in digital business operations across modern en- terprise environments. A mixed-method approach was employed, combining systematic literature analysis with a case-based evaluation involving three digital service companies implementing blockchain and AI models. Quantitative assessment was conducted using performance metrics such as transaction processing time, data integrity validation, and trust perception index, based on survey responses from 120 stakeholders, system log analysis across three digital service companies, and expert validation involving 15 industry specialists. Findings reveal that integrating blockchain with AI improves operational transparency by 42 percent, reduces data verification time by 35 percent, and increases stakeholder trust levels by 48 percent compared to conventional systems. The model demonstrates improved audit trails, decentralized decision-making, and enhanced anomaly detection accuracy in business data processes. The fusion of blockchain and AI offers a promising technological architecture capable of strengthening digital governance, increasing trust, and supporting secure business transformation. This research provides theoretical and practical implications for enterprises adopting emerging technologies toward sustainable and ethical digital operations, aligning strongly with digital transformation goals and SDG principles.
Predicting Supply Chain Risks Using Machine Learning for Resilient Operations Widayanti, Riya; Setiyowati, Harlis; Yusup, Muhamad; Rodriguez, Marta
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1376

Abstract

Rising supply chain disruptions highlight increasing vulnerabilities in global logistics networks caused by geopolitical conflicts, fluctuating demand, transportation failures, and environmental instability. These challenges reveal the limitations of conventional risk assessment approaches that rely heavily on manual analysis and historical data. Machine Learning (ML) offers a promising approach to enhance predictive intelligence and support more accurate decision making in complex supply chain environments. This study aims to develop and evaluate a Machine Learning based risk prediction model capable of identifying potential supply chain disruptions and enabling early detection of critical risk factors in global logistics operations. A quantitative experimental approach was employed using supply chain datasets integrated with disruption indicators from international logistics activities. The dataset consisted of more than 5,000 operational records collected between 2018 and 2024. Several machine learning algorithms were implemented and compared, including Random Forest, Gradient Boosting, and Support Vector Machines. Experimental results indicate that the Gradient Boosting algorithm achieved the highest predictive performance with an accuracy of 94.2%. The model successfully identified key determinants of supply chain risk, including demand variability, supplier reliability, and transportation delays. These findings confirm that machine learning based predictive models can enhance supply chain resilience by enabling early risk detection and supporting proactive decision making in global logistics operations.
Blockchain Augmented AI Architecture for Strengthening Cybersecurity and Operational Trust in Modern Business Ecosystems Novriyanti, Bunga; Mardiana, Mardiana; Lutfiani, Ninda; Moyo, Kgomotso
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1384

Abstract

Modern enterprises increasingly rely on interconnected digital infrastructures, which significantly increase exposure to cyber threats and trust-related vulnerabilities. Traditional security mechanisms often face limitations in providing real-time detection, transparent data validation, and resilient protection across distributed environments. This study aims to develop and evaluate a Blockchain-Augmented AI Architecture to enhance cybersecurity performance and strengthen operational trust within digital business ecosystems. The research adopts a quantitative experimental approach by integrating machine learning–based anomaly detection with a private blockchain layer for secure event logging and tamper-proof verification. The system is tested using simulated enterprise network traffic, and performance is evaluated based on detection accuracy, latency, throughput, and integrity validation efficiency. A comparative analysis is also conducted against conventional centralized cybersecurity models. The results demonstrate that the proposed architecture significantly improves cybersecurity robustness, achieving higher anomaly detection accuracy, reduced false positives, and faster verification time compared to baseline models. Additionally, blockchain integration enhances operational trust by ensuring immutable audit trails, decentralized consensus, and transparent data prove- nance. Overall, the combined system exhibits superior reliability and resilience across simulated network scenarios. This study concludes that integrating AI- driven detection with blockchain technology provides a more secure, transparent, and trustworthy cybersecurity framework for modern enterprises, while of- fering strong potential to support scalable digital transformation and address emerging threats in distributed environments
AI Governance for Sustainable Tech Adoption and Carbon Reduction in Smart Industries Purnama, Suryari; Sunarjo, Richard Andre; Hardini, Marviola; Parker, Jonathan
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1387

Abstract

Rapid advancements in Artificial Intelligence (AI) and sustainable technologies are transforming smart industries, yet many organizations still struggle to establish governance mechanisms that ensure responsible adoption while contributing to carbon-reduction objectives. This study aims to examine how AI governance frameworks support sustainable technology adoption and promote carbon reduction across smart industrial environments. Using a mixed-methods research design, the study integrates a systematic literature review, expert interviews, and quantitative assessment of governance maturity to explore the relationship between governance structures, sustainability practices, and emission reduction outcomes. The empirical data were collected through semi-structured expert interviews and a structured survey involving 150 professionals from manufacturing, logistics, and energy sectors, representing managerial, technical, and governance roles within smart industry environments. The findings reveal that AI governance significantly enhances the effectiveness of sustainable technology deployment, particularly through standardized accountability mechanisms, transparent decision-making models, and proactive risk-management protocols. Organizations with higher governance maturity not only adopt sustainable technologies more efficiently but also demonstrate measurable decreases in operational carbon intensity. These results suggest that robust AI governance serves as a critical enabler for sustainable industrial transformation, ensuring that AI driven innovations align with environmental objectives and long-term strategic value. The study concludes that strengthening AI governance frameworks can accelerate responsible technology integration in smart industries, offering practical pathways for carbon reduction and sustainable competitiveness. Future research is encouraged to investigate cross-industry implementation models and develop governance metrics that better capture environmental impacts in evolving digital ecosystems.
Machine Learning Approaches for Cybersecurity in Distributed Cloud Infrastructures Prayitno, Dzovani Sandy Putra; Wibowo, Shesilia; Widjaya, Irene Apriani; Martono, Aris; Nanle, Zeze
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1417

Abstract

Rapid cloud adoption has transformed enterprise IT infrastructures, but also introduces complex cybersecurity challenges due to the distributed and dynamic nature of cloud environments, increasing exposure to sophisticated cyber threats. This study aims to design and evaluate machine learning-based approaches to enhance cybersecurity in distributed cloud infrastructures, focusing on improving threat detection accuracy, scalability, and operational efficiency in multi-cloud environments. The proposed method employs a layered machine learning framework integrating supervised and unsupervised algorithms to detect intrusions, anomalous behaviors, and policy violations across distributed cloud nodes, supported by real-time data collection and adaptive model training. A methodological illustration indicates that machine learning approaches can achieve higher detection accuracy approximately 90% compared to traditional rule based systems approximately 78%, while reducing false-positive rates from around 22% to 10%, and experimental results further confirm improved detection performance, reduced false positives, and faster response times while maintaining scalability under increasing workloads. These findings demon- strate that machine learning-driven cybersecurity solutions provide a more adaptive, scalable, and effective defense mechanism, supporting secure and sustainable digital transformation in modern cloud environments.
Optimizing Digital Promotional Graphic Design Strategies Using the AIDA Model Rahardja, Untung; Aini, Qurotul; Supriati, Ruli; Al Hafiz, Mohammad Aditya
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1425

Abstract

The rapid growth of digital platforms has intensified competition in online marketing, requiring organizations to adopt more strategic, adaptive, and data-driven promotional approaches. Digital promotional graphic design plays a crucial role in capturing consumer attention and delivering persuasive messages across various platforms. However, differences in platform characteristics and user behavior may influence the effectiveness of these visual strategies, highlighting the need for a more structured design approach. This study aims to optimize digital promotional graphic design strategies by integrating the AIDA model (Attention, Interest, Desire, and Action) into visual communication practices to improve audience engagement and promotional effectiveness. This research employs a qualitative descriptive approach, combining literature review and visual content analysis. Digital promotional designs from various online platforms were systematically analyzed using AIDA indicators, supported by engagement metrics as secondary data to strengthen the evaluation of design effectiveness. The findings indicate that integrating data-driven analytics with the AIDA model significantly enhances the effectiveness of digital promotional graphic design. Visual optimization improves attention, structured and relevant content sustains interest, emotional and value-based messaging stimulates desire, and clear, strategically placed Call To Action (CTA) elements effectively encourage user action. This study concludes that the combination of the AIDA framework and data- driven decision-making provides a comprehensive and strategic foundation for optimizing digital promotional graphic design, ultimately improving communi- cation effectiveness and promotional performance in the digital era.
Analysis of Inorganic Waste Classification Orange Box Based on TensorFlow Lite using Raspberry Pi 5 Aini, Qurotul; Faturahman, Adam; Agustian, Harry; Aritonang, Frengky Jonathan; Zainarthur, Henry
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1428

Abstract

While Smart City initiatives are evolving, waste management infrastructure remains a critical bottleneck, often hindered by high energy dependency and latency issues associated with cloud computing. Traditional automated solutions lack the autonomy required for scalable, outdoor deployment. This research introduces Orange Box a self-sustaining Edge-AI waste classifier designed to bridge the gap between high-performance computing and energy efficiency. The primary goal is to demonstrate that complex Deep Learning tasks can be executed locally on renewable energy without sacrificing classification precision. The system orchestrates a MobileNetV2 architecture on the Raspberry Pi 5, utilizing TensorFlow Lite (TFLite) quantization to drastically reduce computational load. Uniquely, this Green IoT node is fully decoupled from the power grid, driven by a custom power management system utilizing a 100Wp monocrystalline solar panel to sustain both the neural processing unit and robotic actuators. Experimental benchmarks reveal a robust 92% classification accuracy with an inference latency of just 45ms, significantly outperforming previous edge-device generations. Crucially, energy analysis validates operational autonomy for up to 72 hours without sunlight, confirming the system’s reliability for continuous urban deployment. This study demonstrates that the convergence of quantized Edge AI and solar harvesting is not merely theoretical but a deployable standard for the next generation of Smart City infrastructure, directly advancing the Sustainable Development Goals (SDGs) for sustainable urbanization.
Cloud Computing and Artificial Intelligence for Secure and Sustainable Digital Transformation Andayani, Dwi; Lestari Santoso, Nuke Puji; Hua, Chua Toh; Henry, Bintang Nandana
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1439

Abstract

The rapid growth of cloud computing and Artificial Intelligence (AI) has accel- erated digital transformation in business organizations while increasing concerns related to data security and sustainability. Ensuring secure and sustainable digital transformation has therefore become a strategic priority in the current digital economy. This study aims to examine the role of cloud computing and AI in supporting secure and sustainable digital transformation in business operations. This research adopts a quantitative approach by collecting survey data from 300 organizations that have implemented cloud computing and AI technologies. The data are analyzed using Structural Equation Modeling (SEM) to evaluate the relationships between cloud computing adoption AI capability digital security and sustainability performance. The results show that cloud computing adoption has a significant positive effect on secure digital transformation. AI capability also significantly enhances digital security and operational sustainability. Furthermore the integration of cloud computing and AI strengthens organizational readiness for sustainable digital transformation. The findings indicate that organizations leveraging both cloud computing and AI achieve higher levels of digital security and sustainability compared to those adopting these technologies independently. This study provides empirical evidence that integrated cloud and AI strategies are essential for achieving secure and sustainable digital transformation and offers practical implications for managers and policymakers in designing technology driven business strategies.
Governance Frameworks for Sustainable Artificial Intelligence in Digital Business Practices Sunarya, Po Abas; Handra, Tessa; Syahyono, Syahyono; Al-Farouqi, Kamal Arif; Sihotang, Sondang Visiana
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1443

Abstract

The rapid advancement of digital technologies has accelerated organizational transformation across industries, compelling firms to adopt emerging innovations such as Artificial Intelligence (AI) and cloud computing to enhance operational efficiency, scalability, and data-driven decision-making. Despite their growing adoption, challenges related to security, governance, and sustainability often constrain the realization of their full strategic value. This study aims to investigate the integrated role of AI and cloud computing in supporting secure and sustainable digital transformation, particularly in relation to efficiency, governance, and sustainability outcomes aligned with the Sustainable Development Goals (SDGs). A qualitative research design based on a systematic literature review was employed to synthesize insights from prior studies on AI, cloud computing, and digital transformation. The findings indicate that cloud computing provides scalable and secure infrastructure, while AI enhances intelligent automation, predictive analytics, and decision-making capabilities. Furthermore, their integration generates synergistic effects that improve operational efficiency, strengthen data governance, and support sustainability through optimized resource utilization. These results highlight the complementary relationship between cloud computing and AI in enabling secure, efficient, and sus- tainable digital ecosystems. This study contributes to the digital transformation and governance literature by offering an integrated conceptual perspective and provides practical insights for organizations in aligning technological adoption with sustainability and governance objectives.
Digital Banking and Operational Efficiency toward Bank Sustainability Performance Soleman Lenggu, Max ABR.; Muhtarom, Muhtarom; Rangi, Noah; Sunengsih, Meriyana
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1445

Abstract

The rapid growth of digital banking has significantly transformed banking operations by enhancing efficiency and supporting sustainability-oriented practices aligned with the Sustainable Development Goals (SDGs), yet empirical evidence on its integrated impact remains limited, particularly in emerging markets. This study aims to examine the effect of digital banking adoption on operational efficiency and bank sustainability performance, while assessing the role of efficiency in strengthening sustainable outcomes. A quantitative panel data approach is applied using multi-year data from 25 commercial banks over the period of 2018–2023, where digital banking indicators, efficiency measures, and sustainability performance proxies are analyzed through regression-based panel data models with appropriate control variables. The empirical findings reveal that digital banking adoption positively and significantly improves operational efficiency, and higher efficiency levels are associated with enhanced sustainability performance, indicating that efficiency serves as a key mechanism linking digital transformation and sustainable banking performance. This study concludes that digital banking acts as a strategic enabler of sustainable banking by simultaneously improving operational efficiency and long-term sustainability performance, offering important implications for bank managers and policy makers in formulating data-driven digital transformation strategies that support sustainable financial development.

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