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Journal of Digital Market and Digital Currency
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30480981     DOI : https://doi.org/10.47738/jdmdc
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
Journal of Digital Market and Digital Currency publishes high-quality research on: Digital Marketing Digital Currencies Cryptocurrency Trends Blockchain Applications Fintech Innovations Our goal is to provide a platform for researchers, practitioners, and policymakers to share innovative findings, discuss emerging trends, and address the challenges and opportunities presented by the Journal of Digital Market and Digital Currency.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2024): Regular Issue June 2024" : 5 Documents clear
Time Series Analysis of Bitcoin Prices Using ARIMA and LSTM for Trend Prediction Berlilana; Wahid, Arif Mu’amar
Journal of Digital Market and Digital Currency 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/jdmdc.v1i1.1

Abstract

This study investigates the efficacy of ARIMA and LSTM models in predicting Bitcoin prices, emphasizing the importance of accurate price prediction for trading, risk management, and investment strategies in the volatile cryptocurrency market. The objectives are to analyze Bitcoin prices to identify underlying patterns and trends, compare the predictive performance of ARIMA and LSTM models, and provide insights into their practical applications for Bitcoin price prediction. A comprehensive dataset of Bitcoin prices from January 1, 2011, to December 31, 2023, sourced from CoinMarketCap, was used. Data preprocessing included handling missing values, removing duplicates, achieving stationarity through differencing, and normalizing data using MinMaxScaler. The ARIMA model's best-fitting parameters were identified using ACF and PACF plots, and it was trained with the statsmodels library. The LSTM model involved data preparation through windowing and train-test splitting, constructing a neural network with LSTM layers, and training using TensorFlow/Keras. Evaluation metrics included Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with comparisons based on accuracy and computational efficiency. The ARIMA model demonstrated impressive performance with an MAE of 2.308392356829177e-215 and an RMSE of 0.0, indicating a near-perfect fit to the training data. The LSTM model achieved an MAE of 0.00021804577826689423 and an RMSE of 0.00021916977109865863, showing robust performance in handling nonlinear and long-term dependencies. The ARIMA model excelled in computational efficiency with a training time of 2.548070192337036 seconds and a prediction time of 0.0009970664978027344 seconds, while the LSTM model required 378.69622468948364 seconds for training and 0.6859967708587646 seconds for prediction. The results highlight ARIMA's effectiveness in capturing linear trends and its suitability for short-term trading strategies, while LSTM is better for long-term investment strategies due to its ability to model complex patterns. Despite potential overfitting in ARIMA and high computational demands for LSTM, the study suggests exploring hybrid models, incorporating additional data sources, and developing advanced techniques to enhance predictive accuracy in future research.
Analysis of Apriori and FP-Growth Algorithms for Market Basket Insights: A Case Study of The Bread Basket Bakery Sales Hery; Widjaja, Andree E.
Journal of Digital Market and Digital Currency 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/jdmdc.v1i1.2

Abstract

Market basket analysis is a crucial technique in retail for uncovering associations between items frequently purchased together. This study aims to compare the effectiveness of the Apriori and FP-Growth algorithms using sales data from "The Bread Basket" bakery, comprising 20,507 transactions. Key variables include TransactionNo, Items, DateTime, Daypart, and DayType. The data underwent preprocessing steps, including cleaning, tokenization, and feature extraction using TF-IDF. The Apriori and FP-Growth algorithms were implemented with hyperparameter tuning and an 80/20 training/testing split. Performance metrics were evaluated, revealing that Apriori had an execution time of 4.08 seconds and memory usage of 45.36 MiB, whereas FP-Growth exhibited an execution time of 4.15 seconds and significantly lower memory usage at 0.08 MiB. The quality of the association rules was assessed by metrics such as support, confidence, and lift. For example, the Apriori algorithm generated the rule {Alfajores} -> {Coffee} with support 0.018885, confidence 0.520000, and lift 1.087090, while FP-Growth produced the rule {Scone} -> {Coffee} with support 0.017829, confidence 0.519231, and lift 1.085482. FP-Growth generally outperformed Apriori, particularly in memory efficiency, due to its use of the FP-tree data structure, which reduces the need for multiple database scans. The practical implications for "The Bread Basket" bakery include optimizing product placement and inventory management based on the identified associations, such as placing Coffee near Cake or Medialuna to encourage complementary purchases. The study concludes that while both algorithms effectively generate meaningful association rules, FP-Growth's superior memory efficiency makes it more suitable for large datasets. Limitations include data quality and the study's scope, confined to a single bakery. Future research should explore hybrid approaches, real-time data analysis, and applications across different retail sectors to enhance market basket analysis techniques further.
Comparison of K-Means and DBSCAN Algorithms for Customer Segmentation in E-commerce Paramita, Adi Suryaputra; Hariguna, Taqwa
Journal of Digital Market and Digital Currency 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/jdmdc.v1i1.3

Abstract

Customer segmentation is crucial for e-commerce businesses to effectively target and engage specific customer groups. This study compares the effectiveness of two popular clustering algorithms, K-Means and DBSCAN, in segmenting e-commerce customers. The primary objective is to evaluate and contrast these algorithms to determine which provides more meaningful and actionable customer segments. The methodology involves analyzing a comprehensive e-commerce customer dataset, which includes various features such as customer ID, gender, age, city, membership type, total spend, items purchased, average rating, discount applied, days since last purchase, and satisfaction level. Initial data preprocessing steps include handling missing values, encoding categorical variables, and normalizing numerical features. Both K-Means and DBSCAN algorithms are implemented, and their performance is evaluated using metrics such as silhouette score, Davies-Bouldin index, and Calinski-Harabasz score. The results indicate that K-Means achieved a silhouette score of 0.546, a Davies-Bouldin index of 0.655, and a Calinski-Harabasz score of 552.9. In contrast, DBSCAN achieved a higher silhouette score of 0.680, a Davies-Bouldin index of 1.344, and a Calinski-Harabasz score of 1123.9. These findings suggest that while DBSCAN performs better in terms of silhouette score, indicating more distinctly separated clusters, its higher Davies-Bouldin index reflects fewer compact clusters. The discussion highlights that K-Means is suitable for applications requiring clear and well-defined segments of customers, as it produces balanced cluster sizes. DBSCAN, with its strength in identifying clusters of varying densities and handling noise, is more effective in detecting niche markets and unique customer behaviors. This study's findings have significant practical implications for e-commerce businesses looking to enhance their customer segmentation strategies. In conclusion, both K-Means and DBSCAN demonstrate their respective strengths and weaknesses in clustering the e-commerce customer dataset. The choice of algorithm should be based on the specific requirements of the segmentation task. Future research could explore hybrid methods combining the strengths of both algorithms and incorporate additional data sources for a more comprehensive analysis.
Comparative Analysis of Sentiment Classification Techniques on Flipkart Product Reviews: A Study Using Logistic Regression, SVC, Random Forest, and Gradient Boosting Henderi; Siddique, Quba
Journal of Digital Market and Digital Currency 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/jdmdc.v1i1.4

Abstract

Sentiment analysis plays a crucial role in e-commerce, providing valuable insights from customer reviews on platforms like Flipkart. This study aims to compare the effectiveness of various sentiment classification techniques, specifically Logistic Regression, Support Vector Classifier (SVC), Random Forest, and Gradient Boosting. The dataset, collected from Flipkart, consists of 205,052 product reviews spanning various categories. Key data preprocessing steps included handling missing values, removing duplicates, normalizing text, and applying TF-IDF vectorization for feature extraction. We implemented and tuned the hyperparameters for each algorithm using grid search and randomized search. The data was divided into training and testing sets with an 80-20 split, and cross-validation techniques ensured robust model evaluation. The performance of each model was assessed using several metrics: accuracy, precision, recall, F1-score, and ROC-AUC. The results revealed that Logistic Regression achieved an accuracy of 0.8995, precision of 0.8773, recall of 0.8995, an F1 score of 0.8736, and a ROC AUC score of 0.9105. The SVC model showed slightly higher accuracy at 0.8997, precision of 0.8619, recall of 0.8997, and an F1 score of 0.8738. The Random Forest model, while robust, had lower accuracy (0.7953) and struggled with precision (0.6326), recall (0.7953), and an F1 score of 0.7047, but achieved a ROC AUC score of 0.9037. Gradient Boosting performed comparably to Logistic Regression with an accuracy of 0.8993, precision of 0.8512, recall of 0.8993, an F1-score of 0.8735, and a ROC AUC score of 0.9098. Comparative analysis identified SVC and Logistic Regression as top performers, balancing accuracy and computational efficiency. These findings suggest that implementing these models can significantly enhance sentiment analysis in e-commerce, improving customer insights and business strategies. Future research should explore advanced deep learning techniques and address class imbalances to further refine sentiment analysis capabilities.
Predicting Campaign ROI Using Decision Trees and Random Forests in Digital Marketing Hayadi, B Herawan; El Emary, Ibrahiem M. M.
Journal of Digital Market and Digital Currency 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/jdmdc.v1i1.5

Abstract

Digital marketing has become a cornerstone of modern business strategies, leveraging various channels and technologies to promote products and services. Measuring the Return on Investment (ROI) is crucial in evaluating the effectiveness of these marketing campaigns. This study aims to predict the ROI of digital marketing campaigns using two prominent machine learning algorithms: Decision Trees and Random Forests. The primary objective of this research is to compare the performance of Decision Trees and Random Forests in predicting the ROI of digital marketing campaigns. The study focuses on evaluating the accuracy, precision, and robustness of these models, and identifying the key features that influence ROI. The dataset used in this study comprises 200,000 rows and 16 columns, detailing various aspects of digital marketing campaigns, including campaign type, target audience, duration, and channels used. Initial Exploratory Data Analysis (EDA) identified no missing values or duplicates, ensuring a clean dataset for modeling. Data preprocessing involved feature engineering and encoding categorical variables. The models were trained and evaluated using an 80-20 split for training and testing, with cross-validation applied to ensure robustness. The Decision Tree model achieved a Mean Squared Error (MSE) of 1.0896, a Root Mean Squared Error (RMSE) of 1.0439, a Mean Absolute Error (MAE) of 0.8958, and an R2 value of -0.0781. In contrast, the Random Forest model showed superior performance with an MSE of 1.0143, an RMSE of 1.0071, an MAE of 0.8755, and an R2 value of -0.0035. Cross-validation for the Random Forest model yielded a CV MSE of 1.0035, a CV RMSE of 1.0018, and a CV R2 of -0.0039, reinforcing its robustness and accuracy. The Random Forest model's superior performance is attributed to its ability to handle complex interactions between features and its robustness against overfitting. Key predictors such as Conversion_Rate, Acquisition_Cost, and Engagement_Score were identified as significant factors influencing ROI. The study discusses the practical implications of these findings for optimizing digital marketing strategies, acknowledging the limitations of data quality and model assumptions, and suggesting directions for future research, including the integration of additional data sources and exploration of advanced machine learning techniques. This study highlights the potential of machine learning models, particularly Random Forests, in predicting the ROI of digital marketing campaigns. The findings provide valuable insights for marketers to enhance their strategies and optimize budget allocations, emphasizing the importance of predictive analytics in achieving marketing success. Future work should focus on improving model accuracy and exploring new techniques to further advance the field of marketing analytics.

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