Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal Journal of Current Research in Blockchain International Journal Research on Metaverse
Taqwa Hariguna
Magister of Computer Science, Universitas Amikom Purwokerto, Jawa Tengah, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Unsupervised Anomaly Detection in Digital Currency Trading: A Clustering and Density-Based Approach Using Bitcoin Data Taqwa Hariguna; Ammar Salamh Mujali Al-Rawahna
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.12

Abstract

This study investigates the application of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for detecting anomalies in Bitcoin trading data. With the growing significance of Bitcoin in the financial market, identifying irregular trading patterns is crucial for maintaining market integrity and preventing market manipulation. Utilizing a dataset from Kaggle, which includes features such as date, timestamp, open, high, low, close, volume, and number of trades, the data was aggregated from minute-by-minute to hourly intervals for more manageable analysis. The DBSCAN algorithm effectively identified a primary cluster comprising 29,612 data points and flagged 2 points as anomalies, achieving a precision of 1.0, recall of 0.0068, F1-score of 0.0135, and an AUC-ROC of 0.5034. The optimal parameters, determined through sensitivity analysis, were epsilon (ε) = 0.1 and min_samples = 3, yielding the highest silhouette score of 0.21499. These results underscore the algorithm's ability to accurately label anomalies while highlighting the challenge of comprehensive anomaly detection. The study contributes to the field of financial anomaly detection by demonstrating the effectiveness of DBSCAN in analyzing high-dimensional, noisy datasets. It also addresses gaps in the literature regarding the application of density-based clustering methods to Bitcoin trading data. Despite its contributions, the study acknowledges limitations, such as potential data aggregation impact and the need for further validation with different datasets. Future research directions include integrating additional features like social media sentiment and exploring hybrid approaches that combine supervised and unsupervised methods.
In-Depth Analysis of Web3 Job Market: Insights from Blockchain and Cryptocurrency Employment Landscape Calvina Izumi; Taqwa Hariguna
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.4

Abstract

The emergence of Web3, underpinned by blockchain technology, has reshaped the digital realm, ushering in a decentralized and trustless internet paradigm. In this paper, we conduct an extensive analysis of the Web3 job market, leveraging data from 2000 job postings to delineate prevalent keywords, sought-after skills, prevalent job titles, and salary determinants. Our examination reveals compelling insights into the job landscape, showcasing the dominance of technical competencies such as Ethereum proficiency and software development expertise. Among the top skills sought by employers, Ethereum (371 occurrences), React (213 occurrences), NFT (213 occurrences), Java (205 occurrences), and Rust (102 occurrences) prominently feature. Moreover, our analysis uncovers the ascendancy of specialized roles in cybersecurity, technical leadership, and project management, which command premium compensation levels. Notably, security positions emerged as the highest paying roles (average salary: $153,295.86), followed by tech lead (average salary: $121,526.32) and operations (average salary: $120,396.55). These findings offer valuable insights for job seekers, employers, educators, and policymakers navigating the evolving Web3 job landscape. By delineating key trends and challenges, our study contributes to a nuanced understanding of the transformative potential of Web3 and its implications for the future of work.
Co-Authors Ammar Salamh Mujali Al-Rawahna Calvina Izumi