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All Journal Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Aswadi Jaya
PGRI University Palembang
Computer Science & IT Control & Systems Engineering Education Library & Information Science Other

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Decentralized Data Storage Using IPFS for Sustainable Blockchain Availability Improvement Aswadi Jaya; Muh Fahrurrozi; Susy Alestriani Sibagariang; Vinkan Likita; Henry Zainarthur
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.v5i2.860

Abstract

The rapid expansion of digital ecosystems has highlighted the limitations of centralized data storage systems, which often struggle with data loss, censorship, and single points of failure. To address these challenges, this study explores the InterPlanetary File System (IPFS) as a decentralized data management solution that enhances security, availability, and sustainability in distributed information environments. Using the IPFS-KI framework, a descriptive qualitative methodology, this research examines the architectural design, operational mechanisms, and real-world implementations of IPFS. Through literature analysis, node simulations, and case based evaluation, the study investigates IPFS performance in maintaining data integrity, fault tolerance, and resilience against network disruptions and censorship. The findings reveal that IPFS provides improved data reliability, transparency, and scalability compared to conventional centralized architectures, although certain limitations remain in terms of node stability and hidden centralization. This study contributes to a broader understanding of how decentralized storage technologies like IPFS can support the development of more secure, equitable, and sustainable digital infrastructures.
Self Supervised Transformers for High Dimensional Time Series Anomaly Detection Aswadi Jaya; Derlina; Qurotul Aini; Agung Rizky; Richard Evans
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/b-front.v6i1.1078

Abstract

This study addresses anomaly detection in high dimensional time series data within the context of Artificial Intelligence (AI) driven software development, where modern systems generate large temporal data streams and reliable monitoring remains difficult due to noise, complexity, and limited labeled anomalies. The objective of this research is to develop an effective and scalable anomaly detection framework based on self supervised transformer models that can learn meaningful temporal representations without heavy reliance on manual annotation. The proposed method applies self supervised pretraining through masked sequence reconstruction and contrastive temporal learning on large scale, unlabeled multivariate time series datasets, followed by transformer based attention mechanisms to capture long range dependencies and compute anomaly scores. Experiments are conducted using benchmark datasets and real world system log data implemented with Python based deep learning tools and transformer architectures to evaluate detection performance. The results indicate that the proposed approach improves detection accuracy and reduces false positive rates compared to traditional statistical techniques and supervised deep learning models, particularly in high dimensional and low label settings. In conclusion, integrating self supervised learning with transformer architectures provides a robust and generalizable solution for time series anomaly detection, contributing to software analytics and monitoring systems by lowering labeling costs and improving adaptability across application domains.
Transforming Human Resource Practices in the Digital Age: A Study on Workforce Resilience and Innovation Sandy Setiawan; Umi Rusilowati; Aswadi Jaya; Hetilaniar; Rion Wang
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.v2i1.80

Abstract

The rapid advancement of digital technologies has significantly transformed human resource (HR) practices, influencing workforce resilience and organizational innovation. As organizations navigate evolving work environments, the integration of technology-driven HR strategies has become essential for maintaining competitiveness. Traditional HR models are being replaced by more automated and data-driven systems, shaping the future of workforce management. This study aims to examine the intersection of HR practices and digital transformation, with a particular focus on how digital tools enhance workforce resilience and foster organizational innovation. It explores the role of AI-driven talent management systems, data-driven decision-making, and adaptive HR strategies in optimizing recruitment, performance evaluation, and employee engagement. A mixed-method approach was employed, combining qualitative and quantitative analyses. Data was collected through a systematic literature review, multiple case studies, and in-depth interviews with HR professionals across various industries. These methods provided comprehensive insights into the evolving landscape of digital HR practices. The findings highlight the critical role of continuous learning, agile work structures, and active employee engagement in fostering a resilient workforce. The adoption of AI-powered HR tools has proven effective in improving decision-making, employee retention, and performance management, ultimately leading to greater organizational adaptability and innovation. This study concludes that digital transformation in HR is not merely an operational shift but a strategic necessity. By successfully integrating digital tools, businesses can create a more flexible, agile, and responsive work environment, fostering long-term growth and sustainability in an increasingly competitive market.
Optimization of Digital Business to Support MSMEs Growth in the Industry 4.0 Transformation Aswadi Jaya; Farhan Saputra; Derlina; Dwi Nur Ramadhan; Thomas Green
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v3n1.147

Abstract

Digital transformation has become essential for Micro, Small, and Medium-Sized Enterprises (MSMEs) in the Industry 4.0 age in order to improve resilience and competitiveness in spite of restricted resources. The influence of digital optimization techniques on MSME growth is investigated in this study. These tactics include digital marketing adoption, e-commerce platform usage, and digital financial management tools. Data from 100 MSMEs was gathered quantitatively using structured questionnaires, and the correlations between the variables were examined using SmartPLS 3. E-commerce platform usage has the biggest impact, followed by digital financial management tools and digital marketing adoption, according to the results, which show that all digital strategies have a favorable impact on MSME growth. According to the model's R Square value of 0.694 for MSME Growth, the examined strategies account for around 69.4% of the growth variation. These results demonstrate how crucial it is for MSMEs to embrace digital technology in order to increase their market reach, boost operational effectiveness, and fortify financial management. Future research is urged to examine other factors impacting digital adoption and to apply these findings in a variety of sector scenarios. The study concludes that MSMEs must invest in digital skills in order to achieve sustainable development in a competitive digital world.
Co-Authors Agung Rizky Derlina Derlina Dwi Nur Ramadhan Farhan Saputra Henry Zainarthur Hetilaniar Muh Fahrurrozi Qurotul Aini Richard Evans Rion Wang Rusilowati, Umi Sandy Setiawan Sibagariang, Susy Alestriani Thomas Green Vinkan Likita