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p-Index From 2021 - 2026
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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Current Research in Blockchain International Journal Research on Metaverse
Guballo, Jayvie Ochona
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Temporal Analysis of Blockchain Transactions in the Metaverse Using Time Series Guballo, Jayvie Ochona
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.32

Abstract

This study aims to analyze the temporal data of blockchain transactions in the metaverse using time series analysis techniques such as ARIMA and LSTM. The primary focus of this research is to identify significant trends and time patterns in transaction activities within the metaverse. By employing ARIMA, the time series data is decomposed into trend, seasonal, and residual components, providing crucial insights into its structure. The ARIMA model demonstrated a mean absolute error (MAE) of 10,525.73, a mean squared error (MSE) of 150,247,506.45, and a root mean squared error (RMSE) of 12,259.65, indicating a reasonably good fit with some potential for improvement. To capture more complex temporal dependencies in the data, an LSTM model was also applied. The performance of the LSTM model, evaluated using RMSE, was 10.0 for the training set and 15.0 for the testing set. The higher RMSE on the testing set indicates slight overfitting, where the model fits the training data better than unseen data. However, the LSTM model showed strong capability in predicting daily transaction values with fairly high accuracy, despite some minor discrepancies between actual and predicted values. Descriptive statistical analysis of the transaction data revealed that the average daily transaction volume was 108,225.72 with a standard deviation of 8,489.47, indicating significant variability. The daily transaction range spanned from 83,052.86 to 134,869.80, reflecting a wide variation in transaction volume. The results of this study highlight the importance of temporal analysis in understanding blockchain transactions in the metaverse. Insights gained from this analysis can assist in strategic planning and decision-making within the metaverse ecosystem. By further refining model tuning and employing more advanced analysis techniques, predictive accuracy can be enhanced, providing more comprehensive insights and more accurate predictions of transaction behavior.
Cybersecurity and Audit Compliance in Blockchain and Their Implications for System Resilience and Transaction Errors Catamio, Francis G.; Guballo, Jayvie Ochona
Journal of Current Research in Blockchain Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i4.50

Abstract

This study investigates the influence of cybersecurity indicators and audit compliance on transaction reliability and customer trust within blockchain systems. Using a dataset containing daily records of operational and security metrics, the research employs descriptive statistics, correlation analysis, and multiple linear regression to evaluate how key variables—namely security incidents, audit compliance scores, and reported cyberattacks—affect transaction errors and user trust. The analysis reveals that Security Incidents are positively correlated with Transaction Errors per Million (r = 0.64), while Audit Compliance Score (%) shows a negative correlation with transaction errors (r = -0.47) and a positive correlation with Customer Trust Index (r = 0.58). A multiple regression model indicates that approximately 68.3% of the variance in transaction errors is explained by the selected predictors (Adjusted R² = 0.683). Security Incidents are a statistically significant positive predictor (p < 0.01), and Audit Compliance Score (%) is a significant negative predictor (p < 0.05), whereas Cyber Attacks Reported show no statistically significant effect. Visual analyses further confirm these relationships: systems with higher audit compliance scores tend to exhibit fewer errors and greater user trust, while those with frequent security incidents experience higher transactional failures. These findings underscore the importance of integrating both security and audit mechanisms in blockchain risk management frameworks. Future research is recommended to incorporate additional cybersecurity dimensions and explore longitudinal trends across different blockchain architectures.
An Analysis of the Relationship Between Social Media Usage Intensity and Anxiety Levels Among University Students Using a Quantitative Approach Guballo, Jayvie Ochona
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.287

Abstract

The rapid development of social media has significantly influenced students' communication patterns and daily habits. While it offers ease in accessing information and interacting with others, excessive use of social media can negatively affect mental health, particularly anxiety. This study aims to analyze the relationship between social media usage intensity and anxiety levels among university students. A descriptive-correlational quantitative approach was applied using secondary data. The analysis was conducted using the Python programming language through several stages, including data cleaning, descriptive statistics, data visualization, and Pearson correlation testing. The results show a significant positive relationship between the duration of social media usage and students' anxiety levels, with a correlation coefficient of 0.52 and a p-value of 0.003. These findings indicate that the more time students spend on social media, the higher their reported anxiety levels. This study is expected to serve as a basis for promoting digital literacy and raising awareness of the importance of mental health among university students.
Network-Based Anomaly Detection in Blockchain Transactions Using Graph Neural Network (GNN) and DBSCAN Guballo, Jayvie Ochona; Andes, Joy April C.
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.55

Abstract

The increasing volume of blockchain transactions has raised significant concerns regarding the detection of irregular and high-risk activities within decentralized financial ecosystems. Conventional anomaly detection approaches tend to focus on transactional values alone, often neglecting the structural relationships that define user interactions. This study introduces a network-based anomaly detection framework that integrates graph embedding and density-based clustering techniques to identify abnormal transaction behaviours. Using a real-world blockchain transaction dataset consisting of 1,316 unique addresses (nodes) and 2,709 transaction links (edges), a directed network model was constructed to represent the flow of digital assets between users. A Singular Value Decomposition (SVD)-based graph embedding was employed to map network structures into a two-dimensional latent space, followed by DBSCAN clustering to isolate low-density outliers. The results indicate that approximately 34 nodes, or 2.6% of the total, were classified as anomalous, exhibiting unusually high transaction volumes, disproportionate connectivity, or bridging characteristics across distinct communities. These findings demonstrate that combining topological representation learning with unsupervised clustering effectively reveals hidden patterns of irregularity within blockchain networks. The proposed framework provides a computationally efficient and interpretable foundation for future integration with advanced graph learning models, such as Graph Neural Networks (GNN), to enhance fraud detection and risk assessment in decentralized systems.
Co-Authors Andes, Joy April C. Catamio, Francis G.