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All Journal Jurnal Konseling dan Pendidikan Jurnal Ragam Pengabdian
Aldian Yusup
Institut Prima Bangsa
Education Environmental Science Social Sciences Other

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Analysis of digital transformation of public administration in improving the effectiveness of government services in Indonesia Sigit Wahyudi; Aldian Yusup; Muhammad Rizki Perdana
Jurnal Konseling dan Pendidikan Vol. 13 No. 3 (2025): JKP
Publisher : Indonesian Institute for Counseling, Education and Therapy (IICET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/1183900

Abstract

Digital transformation has become a central pillar in reshaping public administration systems worldwide, including in Indonesia, where government institutions are increasingly adopting digital platforms to enhance service quality. Despite strong national policies such as the Electronic-Based Government System (SPBE), significant gaps remain between regulatory frameworks and practical implementation at central and local levels. This study aims to analyze how digital transformation has influenced the effectiveness of government services in Indonesia by examining implementation patterns, enabling factors, persistent barriers, and societal impacts. Using a qualitative literature review approach, the research synthesizes peer-reviewed studies, government documents, and international reports to identify trends in system integration, human resource capacity, technological infrastructure, and governance readiness. The findings reveal that digital transformation has improved service speed, transparency, and citizen experience, as demonstrated in systems such as OSS-RBA, SIPD, and Jakarta’s JAKI application. However, these benefits are uneven due to interoperability issues, inadequate digital competencies among civil servants, fragmented infrastructure, cybersecurity vulnerabilities, and regional budget disparities. Case evidence shows that successful digitalization occurs when strong policy frameworks are supported by institutional commitment, aligned regulations, robust infrastructure, and user-centered service design. Conversely, misaligned legal procedures, partial system adoption, and organizational resistance hinder effectiveness. This study concludes that Indonesia’s digital governance progress is promising yet incomplete, requiring stronger integration, capacity building, and equitable infrastructure development. The analysis contributes to a deeper understanding of how digital transformation can be strategically strengthened to support efficient, transparent, and citizen-centric public administration.
Evaluation   of    Machine   Learning    Implementation    for    Network Intrusion Detection in Distributed IoT Systems Darmin; Wahyudi; Imam Taufik; Aldian Yusup; Ade Hilman Maulana
Jurnal Ragam Pengabdian Vol. 3 No. 1 (Spesial Issue) (2026): "Dharma Samudera"
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/bbpzef78

Abstract

The rapid expansion of Internet of Things (IoT) ecosystems has significantly increased cybersecurity risks due to device heterogeneity, limited computational resources, and distributed network architectures. Traditional security mechanisms are insufficient to address evolving threats such as Distributed Denial of Service (DDoS), botnets, and zero-day attacks. This study aims to evaluate the implementation of machine learning (ML) algorithms for network intrusion detection in distributed IoT systems by examining accuracy, efficiency, and scalability. The research employs a qualitative literature review approach, systematically analyzing reputable journal articles and conference papers related to IoT security, Intrusion Detection Systems (IDS), and machine learning applications. Data were collected through identification, selection, and thematic synthesis of relevant studies, focusing on algorithm types, evaluation metrics, architectural models, and implementation challenges. The results indicate that deep learning models provide superior accuracy in detecting complex and evolving attacks, while traditional machine learning algorithms offer better computational efficiency for edge deployment. Furthermore, distributed and federated learning architectures enhance scalability and reduce communication overhead. A hybrid hierarchical approach integrating edge, fog, and cloud layers is identified as the most effective solution.
Co-Authors Ade Hilman Maulana Darmin Imam Taufik Muhammad Rizki Perdana Sigit Wahyudi Wahyudi