Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Blockchain Frontier Technology (BFRONT)
Aswadi Jaya
PGRI University Palembang, Indonesia
Computer Science & IT Control & Systems Engineering Education Library & Information Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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 : Indonesian Association of Higher Education in Informatics and Computing (APTIKOM)

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 : Indonesian Association of Higher Education in Informatics and Computing (APTIKOM)

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.
Co-Authors Agung Rizky Derlina Henry Zainarthur Muh Fahrurrozi Qurotul Aini Richard Evans Susy Alestriani Sibagariang Vinkan Likita