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All Journal Technomedia Journal ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Dwi Cahyono
University of Muhammadiyah Jember
Humanities Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Strategy to Improve Higher Education Quality through OBE and Benchmarking: Strategi Meningkatkan Kualitas Pendidikan Perguruan Tinggi dengan OBE dan Benchmarking Mohamad Agus Salim; Arthur Simanjuntak; Nova Syahrani Arasid; Indri Mariska Putri; Suhada Suhada; Dwi Cahyono
ADI Pengabdian Kepada Masyarakat Vol 5 No 2 (2025): ADI Pengabdian Kepada Masyarakat
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/adimas.v5i2.1214

Abstract

Growing demands for global competitiveness and accountability push universities to adopt innovative strategies. In the context of higher education in Indonesia, there is a growing recognition of the need to align with international standards to improve quality, relevance, and competitiveness. One promising approach is the adoption of Outcome-Based Education (OBE), which emphasizes clear learning outcomes and continuous improvement, supported by systematic quality assurance. This study examines how OBE, when combined with international benchmarking, can effectively enhance educational quality and institutional performance. The aim is to explore the role of benchmarking in identifying performance gaps and adopting best practices to support sustainable academic improvement. A qualitative research design was employed, utilizing data collected from document analysis, semi-structured interviews, and institutional case studies. These sources provided a comprehensive understanding of the strategies implemented and the institutional changes observed. The findings reveal that integrating OBE with international benchmarking significantly improves curriculum alignment, student centered learning, and internal quality assurance systems. Benchmarking serves as a strategic tool to compare institutional practices with global standards, fostering the adoption of innovative pedagogical and assessment methods. It also contributes to improving academic reputation and international rankings. The integration of OBE and benchmarking provides a practical and sustainable framework for higher education institutions to enhance academic quality and global competitiveness. This model supports continuous institutional improvement and positions Indonesian universities to better meet global challenges in the education sector.  
Self Learning Artificial Intelligence for Autonomous Threat Detection in Computer Networks Dwi Cahyono; Herman Herman; Ikyboy Van Versie
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tk5ypk40

Abstract

The rapid expansion of large-scale computer networks and the exponential growth of big data have significantly increased the complexity and frequency of cyber threats, rendering traditional signature-based security mechanisms inadequate for adaptive detection. This study aims to develop a self-learning AI model capable of autonomously identifying evolving attack patterns and anomalous behaviors in large-scale networks without relying exclusively on pre-labeled datasets. The proposed framework integrates deep neural architectures, incremental learning, and behavior-based traffic analysis to enable continuous adaptation to dynamic threat environments while ensuring computational efficiency and scalability. The model was trained and evaluated using realistic network traffic datasets simulating distributed attacks, zero-day exploits, and advanced persistent threats across heterogeneous environments. Experimental findings demonstrate that the self-learning approach enhances detection accuracy, reduces false positives, and accelerates response times compared to conventional intrusion detection systems. In addition, the combination of deep neural architectures with incremental learning and scalable data processing further strengthens model robustness and adaptability in complex and evolving networks. The results indicate that integrating adaptive AI into cybersecurity frameworks enhances proactive defense capabilities, improves resilience in large-scale computer networks, and provides a scalable, intelligent solution for next-generation threat detection systems. This study highlights the practical relevance of combining AI, big data analytics, and cybersecurity strategies to support intelligent, adaptive security solutions capable of addressing emerging threats, minimizing operational risks, and fostering robust network protection in increasingly complex digital infrastructures.
Blockchain Integration for Secure Data Provenance and Interoperable Database Management Terra Saptina Maulani; Dwi Cahyono; Yansa Sendi Fadillah; Maulidya Reva Aprianti; John Edwards
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v6i1.1025

Abstract

The rapid advancement of digital technologies has led to a significant increase in data volume and complexity, while traditional database systems continue to face challenges in ensuring data security, integrity, transparency, and interoperability across platforms, resulting in higher risks of data tampering, limited audit trails, and the formation of data silos. This study aims to examine and develop a blockchain integration model with conventional database systems to strengthen secure data provenance and enhance interoperability among heterogeneous databases. This research proposes a hybrid architecture that combines on data recording using a permissioned blockchain with off data storage through Relational Database Management System (RDBMS) or Not Only SQL (NoSQL) databases, where blockchain functions as a trust layer that records data hashes, metadata, and immutable change histories, while system evaluation is conducted through security testing, data integrity assessment, auditability analysis, latency measurement, throughput evaluation, data consistency analysis, and cross-platform interoperability testing. The experimental results demonstrate that blockchain integration significantly improves data security and traceability by providing transparent and tamper-resistant audit trails, while enabling secure and consistent data exchange across systems through integration modules and API gateways, despite introducing additional performance overhead compared to conventional database systems. This study concludes that integrating blockchain with conventional database systems is an effective approach for ensuring secure data provenance and interoperable database management, offering a balanced trade-off between security, transparency, and system efficiency, and presenting strong potential for further development in large-scale distributed data environments.
Challenges and Opportunities in Implementing Big Data for Small and Medium Enterprises (SMEs) Dwi Cahyono; Apriani Sijabat; Muktar Bahruddin Panjaitan; Dwi Julianingsih; Agung Lorenzo
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.74

Abstract

Small and Medium Enterprises (SMEs) play a crucial role in the global economy but often face significant challenges when adopting new technologies like Big Data. While Big Data offers opportunities for improving decision-making, operational efficiency, and gaining a competitive edge, many SMEs struggle due to financial constraints, limited technical expertise, and concerns over data security and privacy. This paper explores the challenges SMEs encounter in adopting Big Data and identifies the opportunities it provides for growth and innovation. A mixed-methods approach is employed, combining qualitative interviews with SME managers and quantitative surveys from 150 SMEs to gather comprehensive data. The findings reveal that SMEs face barriers such as high implementation costs and lack of skilled personnel, but they also recognize the potential for Big Data to enhance customer insights, improve business processes, and foster new business models. Recommendations include exploring cost-effective solutions, investing in employee training, strengthening data security, and adopting modular systems that integrate easily with existing operations. This study underscores the importance of overcoming these challenges and leveraging Big Data as a key driver of digital transformation for SMEs, ultimately helping them to compete more effectively in an increasingly data-driven marketplace.
Efficient Machine Learning Acceleration with Randomized Linear Algebra for Big Data Dwi Cahyono; Apriani Sijabat; Kamal Arif Al-Farouqi
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/v3i1.163

Abstract

The rapid growth of big data has significantly increased the computational complexity of machine learning models, particularly due to intensive linear algebra operations that limit scalability and efficiency. This study aims to investigate the effectiveness of Randomized Linear Algebra (RLA) as an acceleration strategy for machine learning in large scale data environments. The research adopts an experimental methodology by integrating randomized techniques such as matrix sketching and random projection into standard machine learning pipelines and evaluating their performance against deterministic baseline approaches. Experiments are conducted on large dimensional datasets using multiple machine learning models, with performance assessed in terms of computational time, memory usage, model accuracy, and scalability. The results demonstrate that the proposed RLA based approach substantially reduces computational cost and memory consumption while maintaining comparable predictive accuracy to conventional methods. These findings indicate that randomized techniques provide an effective trade off between efficiency and accuracy, enabling scalable machine learning for big data applications. In conclusion, this study contributes to the advancement of efficient Artificial Intelligence (AI) systems by demonstrating that RLA can serve as a practical and scalable solution for accelerating machine learning computations in big data contexts, aligning with the growing demand for resource efficient and high performance AI infrastructures.
Development of Digital Technology Based Learning to Enhance Students Strategic Management Competencies Dwi Cahyono; Rohim Rohim; Ardivan Avandi; Chua Toh Hua
Technomedia Journal Vol 11 No 1 (2026): June
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v11i1.2658

Abstract

The advancement of digital technology has become a major driving force behind the transformation of higher education in Indonesia, particularly in developing students' strategic management competencies, where active engagement and critical thinking are essential. This study employed a quasi-experimental approach with a pretest–posttest design to evaluate the effectiveness of technology-enhanced learning that integrates a Learning Management System (LMS), strategic simulation, and gamification elements. The research instruments consisted of a strategic competency test based on business case studies, digital literacy and student engagement questionnaires, and classroom observation sheets. Quantitative data were analyzed using paired-sample t-tests and Pearson correlation analysis, while qualitative data were examined through student feedback. This study addresses a gap in the Indonesian higher education literature, which has predominantly focused on student motivation, satisfaction, or engagement without directly assessing measurable improvements in strategic management competencies. The findings reveal that the simultaneous integration of an LMS, digital simulations, and gamification significantly enhances students' abilities in business environment analysis, strategy formulation, decision-making, and performance evaluation, thereby providing a more practical and strategically oriented learning experience. The novelty of this study lies in proposing a systematic and integrated digital learning model that bridges theoretical concepts and practical application through interactive simulations and gamification. This research contributes academically by enriching the literature on digital higher education in Indonesia and offers practical implications for developing adaptive curricula and data-driven instructional designs that foster active student engagement in the digital era.
Co-Authors Agung Lorenzo Apriani Sijabat Apriani Sijabat Ardivan Avandi Arthur Simanjuntak Chua Toh Hua Dwi Julianingsih Herman Herman Ikyboy Van Versie Indri Mariska Putri John Edwards Kamal Arif Al-Farouqi Maulidya Reva Aprianti Mohamad Agus Salim Muktar Bahruddin Panjaitan Nova Syahrani Arasid Rohim Rohim Suhada Suhada Terra Saptina Maulani Yansa Sendi Fadillah