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All Journal Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Jeffri Prayitno Bangkit Saputra
Doctor of Computer Science Program Bina Nusantara University, Jakarta, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Modeling the Impact of Holidays and Events on Retail Demand Forecasting in Online Marketing Campaigns using Intervention Analysis Jeffri Prayitno Bangkit Saputra; Aayush Kumar
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.v1i2.9

Abstract

This study explores the impact of holidays and events on retail demand forecasting using intervention analysis within a SARIMAX model framework. Retail demand forecasting is critical for inventory management and supply chain optimization. Traditional forecasting models often struggle to account for irregular events like holidays, leading to inaccuracies. This study aims to address these limitations by incorporating holidays and events as exogenous variables in the forecasting model. The dataset, consisting of retail sales records across multiple product categories, was preprocessed to handle missing values and standardize date formats. Binary indicators for state holidays and school holidays were created, along with temporal features like the day of the week and hour of the day. The stationarity of the time series was confirmed using the Augmented Dickey-Fuller (ADF) test, with a statistic of -48.67066391486136 and a p-value of 0.0. The SARIMAX model (1, 1, 1)x(1, 1, 1, 24) was developed and evaluated. The model achieved an Akaike Information Criterion (AIC) of 363321.861 and a Bayesian Information Criterion (BIC) of 363375.269. Key coefficients included the state holiday variable at 0 (p-value: 1.000000) and the school holiday variable at 165.2158 (p-value: 0.919689), though neither were statistically significant. Diagnostic checks revealed significant non-normality and heteroscedasticity in the residuals. Forecasting accuracy was assessed using Mean Absolute Error (MAE: 8057.069376036054) and Mean Squared Error (MSE: 809008799.3517022). The Mean Absolute Percentage Error (MAPE) was not computable due to division by zero. Visualizations comparing forecasted versus actual demand highlighted the model’s strengths in capturing general trends and seasonal patterns but indicated challenges in accurately predicting demand during holidays and events. The study underscores the importance of incorporating holidays and events into demand forecasting models and suggests further refinement and the inclusion of additional variables for improved accuracy. Future research should explore alternative modeling approaches and validate findings across multiple datasets to enhance the generalizability and robustness of the forecasting tools.
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 Aayush Kumar Nadya Awalia Putri