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All Journal International Journal for Applied Information Management Journal of Current Research in Blockchain International Journal Research on Metaverse
Putri, Nadya Awalia
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Analysis of Blockchain Transaction Patterns in the Metaverse Using Clustering Techniques Saputra, Jeffri Prayitno Bangkit; Putri, Nadya Awalia
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
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 Saputra, Jeffri Prayitno Bangkit; Putri, Nadya Awalia
International Journal Research on Metaverse Vol. 1 No. 2 (2024): Regular Issue September
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
Leveraging TF-IDF and Random Forest to Uncover Genre Patterns in Google Books Metadata Putri, Nadya Awalia; Mukti, Bayu Priya
International Journal for Applied Information Management Vol. 5 No. 4 (2025): Regular Issue: December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i4.112

Abstract

This paper presents a machine learning-based approach for classifying books into genres using their descriptions. We employed a Random Forest classifier combined with Term Frequency-Inverse Document Frequency (TF-IDF) to convert text descriptions into numerical features, enabling the classification of books into six genres: Fiction, Literary Criticism, Education, Social Science, Biography & Autobiography, and Unknown Genre. The model was trained and evaluated on a dataset sourced from Google Books, which was preprocessed to remove missing data and clean the text descriptions by eliminating punctuation, numbers, and stopwords. We performed 5-fold cross-validation to assess the model's performance, which resulted in an average cross-validation accuracy of 64.22%. The final model achieved an accuracy of 62.71% on the test set, with the highest recall observed in the "Fiction" genre. The results indicated that the Random Forest classifier was particularly effective in classifying well-represented genres like "Fiction" and "Unknown Genre." However, genres with fewer samples, such as "Social Science" and "Biography & Autobiography," showed poor performance, highlighting the challenges posed by class imbalance and data sparsity. A confusion matrix and classification report revealed these discrepancies, with certain genres being misclassified more often than others. This research demonstrates the feasibility of using machine learning for automated book genre classification, offering significant potential for enhancing book recommendation systems and improving user experience. Despite its promising results, the study's limitations, including data sparsity and genre imbalance, suggest that further work is needed to refine the model. Future research could explore the use of deep learning techniques and the expansion of the dataset to address these issues and improve genre classification accuracy. The potential for automated genre classification in real-world applications, such as book categorization and personalized recommendations, presents an exciting direction for the book industry.
Co-Authors Bangkit, Jeffri Prayitno Mukti, Bayu Priya