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Digital Maturity and Transformation Readiness Across Indonesian Industries Anna, Yane Devi; Ismiyanti, Yulina; Evans, Richard
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/d53ekp67

Abstract

Indonesia is experiencing a rapid digital transformation driven by national initiatives such as Making Indonesia 4.0 and the Digital Economy Roadmap 2030. However, prior studies consistently report uneven digital maturity across industries due to disparities in infrastructure, digital skills, leadership commitment, and strategic alignment. This study adopts a Systematic Literature Review (SLR) to synthesize empirical and conceptual evidence on digital maturity and digital transformation readiness across industries in Indonesia and comparable emerging economies. The review focuses on six dominant dimensions identified across prior studies, namely digital strategy alignment, IT infrastructure, data analytics capability, leadership commitment, employee capability, and innovation culture. The synthesis reveals that banking and financial services exhibit the highest level of digital maturity, followed by manufacturing and logistics. At the same time, MSMEs, education, and healthcare remain in early to developing stages. Leadership commitment, workforce digital skills, and data analytics capability consistently emerge as the most critical drivers of transformation readiness. This study contributes theoretically by integrating fragmented findings on digital maturity into a coherent conceptual framework relevant to emerging economies. Practically, the results support national digital policy development and align with SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), and SDG 9 (Industry, Innovation, and Infrastructure) by emphasizing inclusive digital capability development.
Blockchain Integration to Enhance Federated Learning Model Integrity Anna, Yane Devi; Triandari, Sherli; Anggoro, Sigit; Yolandita, Ardirra; Valerry, Adele
Blockchain Frontier Technology Vol. 5 No. 2 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i2.929

Abstract

Federated Learning is a distributed machine learning approach that enables model training without transferring raw data, thereby preserving user privacy. To improve conciseness, overlapping explanations of FL’s privacy benefits across the Abstract, Introduction, and Literature Review have been consolidated, highlighting its importance in sensitive domains while removing redundancy. This allows greater emphasis on the study’s novelty, particularly the Smart Contract design featuring multi-layer verification and reputation checking mechanisms. Despite its advantages, FL faces significant challenges related to model integrity, including parameter manipulation, model poisoning attacks, and limited trust among participating nodes. This study explores the integration of blockchain technology to address these issues. Leveraging decentralization, immutability, and transparency, blockchain is used to validate model updates, record contributions, and manage node reputation. The study employs a literature review and technical architecture design for a blockchain-integrated FL system. The results indicate that blockchain implementation enhances the reliability and security of FL training, especially in low-trust environments, with strong relevance for healthcare, finance, and IoT applications.