Garuda - Garba Rujukan Digital

Misconceptions of metaverse: from etymology to technology Putawa, Rilliandi Arindra; Izumi, Calvina; Sugianto, Dwi; Ghaffar, Soeltan Abdul
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp314-320

The emergence of the metaverse in society is followed by certain confusions, whereas the line between virtual reality and the metaverse remains unclear. Ironically, this has affected the development of the metaverse itself, focusing more on virtual reality while being one of its side components. This has led to the concept losing popularity compared to artificial intelligence technology. This research is a qualitative study that aims to explore the issues at the root of misconceptions and reconstruct the true meaning of the metaverse itself. This research indicates that the misconception already existed when the term was first used alongside virtual reality technology. The term "meta" refers to a higher reality, whereas the terms "digiverse" or "virtuverse" can be used, considering that the terms "digital" and "virtual" can refer to realities lower than the universe.

Uncovering the Efficiency of Phishing Detection: An In-depth Comparative Examination of Classification Algorithms Sugianto, Dwi; Putawa, Rilliandi Arindra; Izumi, Calvina; Ghaffar, Soeltan Abdul
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.72

This research aims to investigate the potential security risks associated with phishing email attacks and compare the performance of three main classification algorithms: random forest, SVM, and a combination of k-fold cross-validation with the xgboost model. The dataset consists of 18,634 emails, with 7,312 identified as phishing emails and 11,322 considered safe. Through experiments, the combination of k-fold cross-validation and xgboost demonstrated the best performance with the highest accuracy of 0.9712828770799785. The email classification graph provides a visual insight into the distribution of classification results, aiding in understanding patterns and trends in phishing attack detection. The analysis of the ROC curve results indicates that k-fold cross-validation and xgboost have a higher AUC compared to random forest and SVM, signifying a better ability to predict the correct class. The conclusion emphasizes the importance of the combination of k-fold cross-validation and xgboost in enhancing email security, with the potential for increased accuracy through parameter adjustments.

Enhancing Household Energy Consumption Forecasting Using the XGBoost Algorithm with Cross-Validation and Residual-Based Evaluation Sugianto, Dwi; Yulianto, Koko Edy
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.253

Accurate forecasting of household energy consumption plays a crucial role in optimizing energy efficiency, supporting sustainable policy decisions, and improving operational management in smart grid systems. This study enhances conventional XGBoost-based forecasting by integrating cross-validation and residual-based evaluation to ensure model robustness and interpretability. Using a dataset of over 90,000 daily household energy records that include temperature, humidity, and appliance-level usage, a systematic preprocessing pipeline was applied—comprising data cleaning, normalization, temporal feature transformation, and partitioning into training and testing subsets. The proposed model was trained using 10-fold cross-validation to minimize overfitting and validated through residual error analysis to assess stability and bias. Evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), demonstrate superior predictive accuracy, achieving MAE = 0.48, RMSE = 0.64, and R² = 0.9864. Visualization of actual versus predicted consumption and symmetric residual distribution further confirm the model’s reliability. The findings highlight that the enhanced XGBoost model not only achieves high precision but also provides a robust foundation for real-time energy monitoring, anomaly detection, and sustainable household energy management. Future work will integrate SHAP-based interpretability and comparative benchmarking with deep learning approaches.

Temporal Patterns in User Conversions: Investigating the Impact of Ad Scheduling in Digital Marketing Pratama, Satrya Fajri; Sugianto, Dwi
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.10

This study explores the impact of ad scheduling on user conversions by analyzing temporal patterns in user behavior. In the increasingly competitive landscape of digital marketing, optimizing the timing of ad placements is critical for maximizing user engagement and conversion rates. Utilizing a comprehensive dataset from Kaggle, which includes variables such as user ID, ad exposure details, and conversion outcomes, we employed both time series analysis and survival analysis to uncover insights into how different ad scheduling strategies affect conversion rates. The ARIMA model, used for time series analysis, provided reasonable predictive accuracy with a Mean Absolute Error (MAE) of 389.92, Root Mean Squared Error (RMSE) of 463.97, and Mean Absolute Percentage Error (MAPE) of 2.26%. This model effectively identified specific hours and days with higher likelihoods of conversion, particularly during evenings and weekends. On the other hand, the Cox Proportional Hazards model, used for survival analysis, demonstrated superior performance with a concordance index of 0.97, indicating its exceptional ability to predict the timing of user conversions based on various covariates such as the number of ads seen and the specific hours of exposure. The findings suggest that strategic ad scheduling, tailored to align with user temporal behavior, can significantly enhance marketing effectiveness by targeting users during peak conversion periods. These insights offer practical implications for digital marketers aiming to refine their ad delivery strategies to achieve higher conversion rates and improve return on investment.

Analysis of the Relationship Between Trading Volume and Bitcoin Price Movements Using Pearson and Spearman Correlation Methods Hananto, Andhika Rafi; Sugianto, Dwi
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.v1i1.8

This study investigates the relationship between trading volume and Bitcoin price movements using Pearson and Spearman correlation methods. The aim is to determine if trading volume can reliably predict Bitcoin price changes. Using a comprehensive dataset of daily Bitcoin prices and trading volumes, various statistical techniques were employed. Pearson and Spearman correlation analyses revealed very weak and statistically insignificant relationships, with correlation coefficients of -0.023788 and 0.021093, respectively. Linear regression analysis further supported these findings, showing an insignificant regression coefficient for trading volume and a very low R-squared value of 0.000566. Volatility analysis, measured by the standard deviation of daily returns, demonstrated high price volatility, consistent with the cryptocurrency market's nature. This volatility is influenced by factors such as market sentiment, regulatory developments, and macroeconomic events. The study also utilized 30-day moving averages to smooth short-term fluctuations and highlight long-term trends in trading volume and closing prices, revealing underlying trends not visible in daily data. A 1-day lagged correlation analysis indicated a very weak relationship (0.008145) between trading volume on one day and price changes on the next, suggesting other factors drive price movements. Visualizations, including time series graphs, histograms, moving averages, and volatility graphs, further illustrated the lack of a clear pattern between trading volume and price changes. In conclusion, trading volume is not a significant predictor of Bitcoin price movements, highlighting the need for comprehensive analytical approaches considering multiple variables to understand and predict Bitcoin price dynamics better.

Exploring User Experience and Immersion Levels in Virtual Reality: A Comprehensive Analysis of Factors and Trends Putawa, Rilliandi Arindra; Sugianto, Dwi
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.3

Virtual Reality technology has advanced rapidly in recent years, opening up new opportunities in various fields from entertainment to education. This research aims to investigate the factors influencing users' level of immersion in VR environments. Data were collected from 500 different VR users regarding their age, gender, play duration, VR headset used, and perceived motion sickness level. Analysis was conducted to evaluate the demographic distribution of users, immersion levels, play duration, and motion sickness levels. The research findings indicate that the majority of VR users are aged between 30-40 years old, with 42% of users aged 30 to 36 and 38% aged 37 to 44. Immersion levels are predominantly moderate to high, with 48% of users reporting level 3 immersion and 28% reporting level 4 immersion. Longer play durations tend to correlate with higher immersion levels, with the average play duration being 27 minutes for users with level 4 immersion compared to 18 minutes for users with level 2 immersion. Higher motion sickness levels are associated with lower immersion levels. The average motion sickness level is 2.5 for users with level 1 immersion and 1.8 for users with level 4 immersion. Additionally, the Oculus Rift VR headset proves to be the top choice for users, with 45% of the total sample using this headset and reporting an average immersion level of 3.8. This is followed by PlayStation VR with 30% of users and an average immersion level of 3.5, and HTC Vive with 25% of users and an average immersion level of 3.6. These findings provide valuable insights into users' preferences and experiences in VR environments, as well as highlighting the importance of considering factors such as age, play duration, and VR headset type in content development and interaction design. By gaining a deeper understanding of human-computer interaction dynamics in virtual environments, this research is expected to make a meaningful contribution to the future development of VR technology.

Geospatial Analysis of Virtual Property Prices Distributions and Clustering Sugianto, Dwi; Hananto, Andhika Rafi
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.v1i2.10

This paper presents an analysis of property prices in the virtual world, focusing on geographical distribution and district comparisons. Utilizing a dataset of virtual properties, we applied scatter plot analysis, cluster analysis using DBSCAN, and box plot comparison to identify key patterns and opportunities within this market. The scatter plot analysis revealed that property prices are unevenly distributed, with higher prices clustering in specific regions, indicating areas of higher desirability and value. The DBSCAN clustering identified distinct high-value clusters, each containing 10 to 67 properties, and highlighted 1,067 properties as noise, suggesting a dispersed distribution of lower-value properties. Box plot comparisons across districts showed significant variations in property values. Some districts exhibited higher median prices, with the highest at 35,452.60 MANA, while others had lower medians. Variability within districts varied, with some showing a wide range of prices and others more uniform values. Outliers suggested unique investment opportunities in both premium and undervalued properties. For virtual real estate investors, the findings emphasize the importance of location and strategic investment. High-value districts and emerging areas offer potential for significant returns. Developers and urban planners can use these insights to focus on high-demand areas, enhancing project value through strategic investments in infrastructure and amenities. This study highlights the dynamic nature of the virtual real estate market and the importance of ongoing research to understand factors influencing property values. Stakeholders can make informed decisions and capitalize on opportunities in this evolving market.

Sentiment Analysis of User Reviews on Cryptocurrency Trading Platforms Using Pre-Trained Language Models for Evaluating User Satisfaction Javadi, Milad; Sugianto, Dwi; Sarmini
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.46

The study examines user sentiment on the Indodax cryptocurrency trading platform using pre-trained Indonesian language models for sentiment analysis. A dataset of 25,000 user reviews was analyzed, revealing that most reviews expressed neutral sentiment, with positive sentiments accounting for 20% and negative sentiments under 4%. The sentiment classification models used include Support Vector Machine (SVM), Logistic Regression, and Naive Bayes. SVM achieved the highest predictive accuracy at 94.22%, followed by Logistic Regression at 93.62%. These models classified sentiments based on TF-IDF feature extraction, highlighting SVM's effectiveness in sentiment classification within the user reviews. Additionally, sentiment trends over time were analyzed, showing fluctuations in user satisfaction corresponding with market events and platform changes, emphasizing the importance of maintaining platform stability during high volatility. The study’s findings suggest actionable improvements for Indodax, such as addressing user concerns that lead to negative sentiments, like customer service and technical issues, while reinforcing platform strengths, such as ease of use. These insights enable Indodax to enhance user satisfaction and retention by monitoring sentiment trends and adjusting features accordingly. However, the study faces limitations due to the use of pre-trained models that may not fully capture Indonesian language nuances and the absence of demographic data, which limits the analysis to general sentiment trends. Future research could incorporate demographic insights and user behavior metrics to offer a more personalized understanding of user sentiment, ultimately aiding Indodax in delivering a more tailored and satisfying user experience.

Classifying Vehicle Categories Based on Technical Specifications Using Random Forest and SMOTE for Data Augmentation Sugianto, Dwi; Wahyuningsih, Tri
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.v5i4.113

This study investigates the application of machine learning for classifying vehicles based on their technical specifications using the Random Forest algorithm. The objective was to create a robust classification model capable of categorizing vehicles into six distinct classes: Hybrid, SUV, Sedan, Sports, Truck, and Wagon. The analysis was conducted using a comprehensive dataset that included features such as engine size, horsepower, weight, and fuel efficiency, along with the target variable, vehicle class. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to balance the training data. The results showed that the model performed particularly well in classifying Sedans, achieving a perfect recall and high F1-score, while struggling with underrepresented classes like Hybrid and Wagon. Despite applying SMOTE, the model’s performance for minority classes remained suboptimal, highlighting the challenges associated with highly imbalanced datasets. The study contributes to the field of vehicle classification by demonstrating the use of Random Forest for such tasks and providing insights into the challenges posed by imbalanced class distributions. The findings underscore the importance of feature selection, especially regarding numerical attributes such as horsepower and engine size, in improving classification accuracy. However, the study also identified limitations, including potential biases in the dataset and the difficulty in improving performance for minority vehicle classes. Future research should explore alternative algorithms like XGBoost or deep learning models, and consider expanding the dataset to include more diverse vehicle types. The practical implications of this work are significant for vehicle market segmentation, offering valuable insights for manufacturers, dealerships, and analysts seeking to optimize vehicle classification and improve market targeting strategies.

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