Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal IAIC Transactions on Sustainable Digital Innovation (ITSDI) Journal of Computer Science and Technology Application
Komara, Maulana Arif
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology Other

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

Found 2 Documents
Search

 1 
Optimization of Machine Learning Algorithms for Fraud Detection in E-Payment Systems Rizky, Agung; Gunawan, Ahmad; Komara, Maulana Arif; Madani, Muchlisina; Harris, Ethan
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.v2i1.68

Abstract

This study explores the optimization of machine learning algorithms for fraud detection in electronic payment (e-payment) systems. The rapid growth of e-payment platforms has introduced significant challenges in ensuring the security and integrity of financial transactions. Fraud detection plays a pivotal role in mitigating these risks, and the application of machine learning (ML) has emerged as a powerful tool to identify fraudulent activities. This research examines how Data Quality (DQ), Algorithm Selection (AS), and Optimization Techniques (OT) influence Model Performance (MP) and, subsequently, Fraud Detection Effectiveness (FDE). The study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 3 to analyze the relationships between these variables. The results demonstrate that high Data Quality significantly enhances Model Performance, while Algorithm Selection and Optimization Techniques also contribute positively, albeit to a lesser extent. The findings reveal that Model Performance plays a crucial mediating role between these factors and the effectiveness of fraud detection. Fraud Detection Effectiveness is found to be significantly impacted by Model Performance, suggesting that improving model accuracy and efficiency is essential for better fraud detection outcomes. Reliability and validity tests show strong internal consistency for all constructs, with Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE) all reaching satisfactory levels. The study highlights the importance of data preprocessing, the careful selection of machine learning models, and optimization techniques in achieving high-performing fraud detection systems. The results provide valuable insights for the development of more robust and scalable fraud detection mechanisms in e-payment systems, contributing to the broader field of machine learning and cybersecurity. Future research could explore advanced techniques like deep learning and blockchain integration for further enhancement of fraud detection systems.
Leveraging IPFS to Build Secure and Decentralized Websites in the Web 3.0 Era Maulana, Imam Ryan; Rahardja, Untung; Azizah, Nur; Rakhmansyah, Mohamad; Komara, Maulana Arif
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 7 No 1 (2025): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v7i1.700

Abstract

In recent years, Web 3.0 has gained significant attention due to its potential to create a more secure and decentralized internet. The background of this research lies in the growing demand for data privacy and security, which traditional Web 2.0 platforms fail to provide.The objective of this study is to explore how IPFS (InterPlanetary File System) can be leveraged to build decentralized websites that prioritize security and user privacy in the Web 3.0 ecosystem. The method involves a qualitative approach, including a case study where IPFS is utilized to develop a decentralized website, followed by a series of performance and security tests. The performance tests revealed that IPFS-based websites achieved a 99.2% uptime compared to 96.5% in traditional websites, and reduced server failure rates by approximately 35%. These quantitative results confirm that IPFS provides higher resilience against data breaches and server failures while reducing reliance on single points of failure. The conclusion drawn from this research indicates that IPFS is a promising technology for developing secure, decentralized websites in the Web 3.0 era, offering an enhanced user experience with improved privacy, data security, and scalability. The findings suggest that adopting IPFS for web development could pave the way for the next generation of decentralized applications, contributing to the ongoing transformation of the internet.
Co-Authors Gunawan, Ahmad Harris, Ethan Madani, Muchlisina Maulana, Imam Ryan Nur Azizah Rakhmansyah, Mohamad Rizky, Agung Untung Rahardja