Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal ADI Journal on Recent Innovation (AJRI) Blockchain Frontier Technology (BFRONT)
Richard Evans
Unknown Affiliation
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Industrial & Manufacturing Engineering Library & Information Science

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Optimizing Smart Contracts and Blockchain for Sustainable Digital Fashionpreneurship Santa Lusianna Sitorus; Ratna Tri Hari Safariningsih; Aldo Hermaya Aditiya Nur Karsa; Maulana Arif Komara; Richard Evans
Blockchain Frontier Technology Vol. 5 No. 1 (2025): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i1.814

Abstract

The digital fashion industry is undergoing a major transformation with the integration of technologies such as blockchain and smart contracts, which offer solutions to key challenges such as product authenticity, intellectual property protection, and supply chain efficiency. This research focuses on the application of blockchain technology and smart contracts in digital fashion entrepreneurship, with the aim of increasing transaction efficiency, product security, and business sustainability. Method used in this study is a quantitative approach with a structural model based on Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the influence between smart contract adoption, digital transaction efficiency, product security, and fashionpreneurship sustainability. Data were collected from digital fashion actors involved in the blockchain-based ecosystem. Research result shows that the adoption of smart contracts has a positive effect on transaction efficiency and product security. In addition, blockchain has been shown to increase supply chain transparency that supports the sustainability of digital fashion businesses. Although this technology faces technical challenges such as high transaction speed and costs, solutions such as layer-2 technology can improve blockchain performance. The conclusion blockchain and smart contract technology can be an effective solution in increasing efficiency, security, and sustainability in the digital fashion industry. Although there are technical challenges that need to be overcome, the application of this technology has great potential to create a more environmentally friendly and transparent digital fashion ecosystem.
AI Enabled Cybersecurity Framework for Multi Cloud Business Environments Sunarjo, Richard Andre; Arif Andika; Ninda Lutfiani; Richard Evans
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1312

Abstract

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.
Co-Authors Agung Rizky Aldo Hermaya Aditiya Nur Karsa Arif Andika Aswadi Jaya Derlina Maulana Arif Komara Ninda Lutfiani Qurotul Aini Ratna Tri Hari Safariningsih Santa Lusianna Sitorus Sunarjo, Richard Andre