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All Journal Blockchain Frontier Technology (BFRONT) International Transactions on Artificial Intelligence (ITALIC)
Agung Rizky
University of Raharja
Computer Science & IT Control & Systems Engineering Education Library & Information Science

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Enhancing User Login Efficiency via Single Sign-On Integration in Internal Quality Assurance System (eSPMI) Maulana Yusuf; Muhamad Yusup; Reza Dani Pramudya; Ahmad Yadi Fauzi; Agung Rizky
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.556

Abstract

In the continually evolving digital era, authentication system efficiency has become crucial for accelerating processes and enhancing user security. This research aims to analyze the integration of Single Sign-On (SSO) features in authentication systems and its impact on user login efficiency, incorporating Artificial Intelligence (AI) concepts. Using a comparative analysis between traditional authentication systems and those integrated with SSO, sample data from three major technology companies show that SSO integration reduces average login time by 60% and increases user satisfaction by 70%. Additionally, integrating AI in SSO systems enhances security by providing predictive analytics for potential security threats and optimizing the overall user experience. These findings suggest that broader adoption of AI-enhanced SSO can significantly strengthen security and efficiency in corporate authentication systems, making it a valuable strategy for organizations aiming to improve user satisfaction and data protection.
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 Ahmad Yadi Fauzi Aswadi Jaya Derlina Maulana Yusuf Muhamad Yusup Qurotul Aini Reza Dani Pramudya Richard Evans