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All Journal Journal of Current Research in Blockchain International Journal Research on Metaverse
Nadya Awalia Putri
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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Analysis of Blockchain Transaction Patterns in the Metaverse Using Clustering Techniques Jeffri Prayitno Bangkit Saputra; Nadya Awalia Putri
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.v1i1.10

Abstract

This study investigates the application of various clustering techniques on a metaverse transaction dataset to identify patterns and groupings. The clustering algorithms evaluated include K-Means, DBSCAN, Gaussian Mixture Model (GMM), Mean Shift, Spectral Clustering, and Birch. The performance of these algorithms is assessed using three metrics: Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. Among these algorithms, K-Means demonstrated the best overall performance, achieving the highest Silhouette Score (0.4702) and Calinski-Harabasz Index (151946.29), as well as the lowest Davies-Bouldin Index (0.6600), indicating well-defined and compact clusters. DBSCAN, while flexible, showed lower performance with a Silhouette Score of 0.1673, a Davies-Bouldin Index of 1.0084, and a Calinski-Harabasz Index of 4231.19. GMM achieved a Silhouette Score of 0.2453, a Davies-Bouldin Index of 1.3626, and a Calinski-Harabasz Index of 23011.20. Spectral Clustering had a Silhouette Score of 0.1668, a Davies-Bouldin Index of 2.0986, and a Calinski-Harabasz Index of 11830.24. Birch achieved a Silhouette Score of 0.2363, a Davies-Bouldin Index of 1.4967, and a Calinski-Harabasz Index of 21375.76. Mean Shift could not provide valid performance metrics. Visualizations, including histograms, box plots, and count plots, provided additional insights into the distribution of numerical features and cluster characteristics. This study highlights the need for tailored clustering approaches and suggests future research directions in hybrid models as well as the impact of feature selection and scaling methods on clustering outcomes.
The Impact of Market Activity on Property Valuations in Digital Real Estate Through a Quantitative Analysis of Bidding and Sales Dynamics Jeffri Prayitno Bangkit Saputra; Nadya Awalia Putri
International Journal Research on Metaverse Vol. 1 No. 2 (2024): Regular Issue September 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i2.11

Abstract

This study investigates the impact of market activities, specifically the number of bids and sales, on property prices in digital real estate markets. With the rise of virtual environments and digital assets, understanding the factors that drive property valuations in these markets has become increasingly important. Utilizing a dataset of 2,000 property transactions, this research employs correlation and regression analyses to explore how competitive bidding and sales frequency influence prices. The results indicate a significant positive correlation (r=0.38r = 0.38r=0.38) between the number of bids a property receives and its final sales price, suggesting that properties attracting more bids are perceived as more valuable, leading to higher prices. The regression analysis further supports this, showing that each additional bid is associated with an increase of 6.63×10216.63 \times 10^{21}6.63×1021 in the sales price (p
Co-Authors Jeffri Prayitno Bangkit Saputra