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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 98 Documents
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Strategic Integration of Cloud Cybersecurity for Resilient Digital Business Transformation Erika, Erika; Hari Safariningsih, Ratna Tri; Cahyono, Dwi; Rangi, Noah
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1313

Abstract

The increasing reliance on cloud computing technologies has driven digital business transformation across various industries, offering scalability, flexibility, and cost efficiency. However, this rapid adoption has simultaneously introduced complex cybersecurity challenges, including data breaches, unauthorized access, and system vulnerabilities that threaten operational continuity and trust. In response to these growing concerns, this study aims to analyze and develop effective cloud based cybersecurity strategies that enhance the resilience of digital business operations during transformation processes. The objective of this research is to identify and evaluate strategic cybersecurity practices that can strengthen cloud infrastructure during digital change. Employing a mixed method approach, the research combines an extensive literature review with qualitative expert interviews involving IT professionals and cybersecurity practitioners from cloud reliant enterprises. A total of 126 participants were involved in the data collection phase, comprising 98 valid survey respondents and 28 interview informants. The results reveal that while many organizations have implemented basic cloud security protocols, only a few have adopted comprehensive, adaptive frameworks capable of withstanding evolving cyber threats. Key strategic elements identified include the integration of zero trust architecture, continuous risk assessment, multi factor authentication, cloud native security tools, and regular employee training. Furthermore, organizations that embedded cybersecurity planning into their digital transformation roadmap demonstrated higher resilience, faster recovery from incidents, and stronger regulatory compliance. Based on these findings, the conclusion emphasizes that a proactive, strategy driven approach to cloud cybersecurity not only mitigates risks but also strengthens the long term sustainability of digital transformation efforts. The proposed framework serves as a guide for decision makers seeking to secure their cloud infrastructure while maintaining agility and innovation in an increasingly digital and threat prone business environment.
Enhancing Trust and Efficiency in E-Commerce Transactions through Blockchain AI Synergy Rahardja, Untung; Daeli, Marda Leni; Anjani, Sheila Aulia; Pasha, Lukita; Asri, Asri; Zainarthu, Henry
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1317

Abstract

Integrating AI-Driven Predictive Analytics and Smart Contracts for Data-Driven Supply Chain Risk Management Pujiati, Tri; Kamil, Mustofa; Silawati, Nur; Ikhsan, Ramiro Santiago
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1318

Abstract

Global supply chains face increasing uncertainty, while traditional risk management often lacks adaptability. This study investigates how AI driven predictive analytics and smart contracts enhance resilience, using mixed methods with case studies and big data analysis. A mixed method approach was employed, combining big data analytics from supply chain networks with machine learning models for predictive forecasting, supported by case studies from multinational manufacturing and logistics companies as well as secondary data from industry reports. The findings reveal that AI driven predictive models significantly improve demand forecasting accuracy, identify potential disruptions earlier, and enhance supplier risk assessment compared to conventional approaches, while integrating data from IoT enabled devices provides real time visibility across logistics operations. Overall, AI powered predictive analytics demonstrates substantial potential in transforming risk management within global supply chains by enabling proactive strategies and resilience, allowing organizations to reduce vulnerabilities, optimize performance, and strengthen competitiveness in dynamic markets, with future research suggested to explore the integration of blockchain for transparency and ethical governance in supply chain ecosystems.
Decentralized Decision Intelligence Using AI and Blockchain in Modern Enterprises Ayubi, M. Nizar; Anggoro, Sigit; Kareem, Yasir Mustafa
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1320

Abstract

In the era of digital transformation, enterprises face increasing pressure to enhance transparency, operational efficiency, and trust in their decision making processes, especially in complex, data-intensive environments. While prior studies have separately explored the roles of Artificial Intelligence (AI) and Blockchain, few have examined their combined impact in creating decentralized and intelligent decision systems within real enterprise contexts. This study introduces a novel conceptual integration model that merges AI-driven analytics with blockchain-based validation mechanisms to enable transparent, traceable, and autonomous decision-making. By synthesizing AI predictive and analytical capabilities with blockchain immutable and distributed architecture, this research extends recent studies (2021-2025) by demonstrating how such convergence can eliminate central dependencies, enhance digital trust, and support data governance across departments. A qualitative case study approach was used to analyze organizations adopting AI blockchain frameworks, and the findings reveal new insights on interoperability, adaptive governance, and smart contract-driven autonomy. The study originality lies in its emphasis on the AI Blockchain synergy as a unified decision-intelligence infrastructure, contributing to the growing discourse on ethical and resilient enterprise systems.
Data Driven Predictive Maintenance Framework for Railway Safety in Indonesia Pasurangga, Dimas; Baltasar, Sora
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1322

Abstract

Indonesia’s railway network faces increasing operational pressures as passenger and freight volumes continue to rise, revealing the limitations of reactive maintenance approaches and emphasizing the need for predictive, data driven safety mechanisms. This study aims to develop a conceptual framework for a data-driven predictive maintenance system to enhance railway safety, reliability, and operational efficiency in Indonesia. A Systematic Literature Review (SLR) was conducted on international and national studies presents 25 key references published between 2016 and 2025 on early detection systems in railway, focusing on railway condition monitoring, IoT based maintenance, and AI driven safety analytics. The synthesized findings indicate that integrating IoT sensors, vibration monitoring, and hot box and hot axle detection systems supported by artificial intelligence and big data analytics can significantly improve early anomaly detection, predictive decision-making, and risk prevention. Nevertheless, several challenges remain, including limited technical capacity, fragmented regulations, and high implementation costs. The proposed data driven predictive maintenance framework positions early detection systems as strategic instruments for digital transformation in railway operations, strengthening risk management, promoting sustainable infrastructure, and aligning Indonesia’s railway governance with global standards for intelligent and resilient transportation systems.
Optimizing Business Workflow Using AI Integrated Blockchain Platforms Djamali, Muhammad Fadheel; Lusiana, Dewi; Parastry, Annisa; Al-Kamari, Omar Arif
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1323

Abstract

In today’s fast evolving digital economy, business workflows often suffer from inefficiencies, data silos, and security vulnerabilities, particularly in environments relying on legacy systems and centralized control. To address these challenges, this study investigates the integration of Artificial Intelligence (AI) and blockchain technologies as a unified platform for enhancing workflow efficiency, transparency, and security across business operations. The primary objective of this research is to analyze how combining AI’s predictive and automation capabilities with blockchain’s decentralized and immutable ledger can optimize key workflow processes such as approval cycles, data validation, and task automation. This study adopts a qualitative case study approach, supported by system modeling and comparative analysis between conventional workflows and AI blockchain enabled systems within a mid sized logistics enterprise. The findings reveal that the integrated platform significantly reduces processing time, enhances traceability, and minimizes errors, especially in interdepartmental transactions and decision making processes. In addition, the use of smart contracts triggered by AI based insights eliminates redundant steps, enabling real time process adaptation. These results confirm that the fusion of AI and blockchain delivers measurable improvements in workflow optimization, offering a scalable and secure foundation for digital transformation. In conclusion, this research demonstrates that AI integrated blockchain platforms not only optimize operational workflows but also provide strategic value for organizations seeking long term agility and resilience in the face of rapid technological and market shifts.
Leveraging Big Data Analytics to Strategically Expand Digital Microcredit Access for MSMEs Rizky, Agung; Ramaditya, Muhammad; Kamal, Abdullah Arif
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1325

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a pivotal role in driving economic development and job creation, especially in emerging economies. However, limited access to formal credit remains a persistent challenge due to the reliance on conventional financial assessments that often exclude MSMEs with informal or incomplete financial histories. This study aims to investigate how big data analytics can be effectively leveraged to strategically expand digital microcredit access for MSMEs, offering more inclusive and accurate credit evaluation models. The research adopts a qualitative descriptive methodology, incorporating a comprehensive literature review and multiple case studies of fintech platforms that utilize alternative data sources such as e commerce transactions, mobile phone activity, utility bill payments, and social media engagement to construct alternative credit scoring systems. The findings indicate that big data enables improved risk profiling, faster loan processing, and wider financial inclusion by reaching unbanked and underbanked MSMEs. Additionally, the integration of machine learning algorithms in analyzing real time behavioral data enhances decision making precision and operational efficiency in digital lending. However, the study also raises critical issues regarding data privacy, ethical use, and transparency in automated credit decisions. In conclusion, the use of big data analytics offers transformative potential to reshape digital microcredit services, empowering MSMEs through accessible, scalable, and intelligent financial solutions that align with broader goals of sustainable economic inclusion and digital transformation.
Optimization of Community Entrepreneurship through Diversification and Digitalization of Locally Based Chocolate Beverages Mulyati, Mulyati; Zebua, Selamat; Apriani, Desy; Oganda, Fitra Putri; Fitriani, Anandha; Sihotang, Sondang Visiana; Nesti Anggraini Santoso
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1328

Abstract

Community-based entrepreneurship plays a vital role in strengthening local economies, particularly in the food and beverage sector where unique and innovative products are highly valued. Among these, locally based chocolate beverages present a promising opportunity for business growth. This study aims to examine how product diversification, digitalization, and consumer behavior influence the performance of entrepreneurs in the local chocolate beverage industry. A quantitative research approach was employed, with data collected through a structured survey involving 150 entrepreneurs and consumers directly engaged in the sector. The collected data were analyzed using statistical methods to evaluate the relationships between the three independent variables product diversification, digitalization, and consumer behavior and entrepreneurship performance as the dependent variable. The results demonstrate that all three factors have a significant and positive effect on entrepreneurship performance. Specifically, businesses that adopt product diversification strategies are better able to reach new consumer segments, reduce business risks, and strengthen brand identity. Meanwhile, the integration of digital technologies enhances market visibility, customer engagement, and operational efficiency. Consumer behavior further acts as a determinant of purchasing patterns, preferences, and loyalty, which in turn shape business strategies. The findings highlight that entrepreneurs who actively embrace diversification, digitalization, and consumer oriented approaches are more likely to achieve sustainable growth and competitive advantage. This research contributes to the academic discourse on entrepreneurship and provides practical insights for entrepreneurs, policymakers, and stakeholders in developing resilient community-based enterprises.

Page 10 of 10 | Total Record : 98

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