Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Dwi Sugianto
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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 1 
Temporal Patterns in User Conversions: Investigating the Impact of Ad Scheduling in Digital Marketing Satrya Fajri Pratama; Dwi Sugianto
Journal of Digital Market and Digital Currency 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/jdmdc.v1i2.10

Abstract

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 Andhika Rafi Hananto; Dwi Sugianto
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.v1i1.8

Abstract

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 Rilliandi Arindra Putawa; Dwi Sugianto
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.v1i1.3

Abstract

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 Dwi Sugianto; Andhika Rafi Hananto
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.v1i2.10

Abstract

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.
Co-Authors Andhika Rafi Hananto Rilliandi Arindra Putawa Satrya Fajri Pratama