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 International Journal Research on Metaverse
Taqwa Hariguna
Magister of Computer Science, Computer Science Faculty, Universitas Amikom Purwokerto, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Customer Segmentation and Targeted Retail Pricing in Digital Advertising using Gaussian Mixture Models for Maximizing Gross Income Taqwa Hariguna; Shih Chih Chen
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.v1i2.11

Abstract

This study investigates the application of Gaussian Mixture Models (GMM) for customer segmentation and targeted pricing strategies in the retail industry to maximize gross income. Using a dataset of 1000 transaction records, the analysis focused on attributes such as unit price, quantity, total amount, and payment methods. The dataset was preprocessed to handle missing values, encode categorical features, and scale numerical features. The optimal number of components for the GMM was determined using the Bayesian Information Criterion (BIC), resulting in the selection of 10 clusters. Model training was conducted using the Expectation-Maximization (EM) algorithm, achieving convergence after 18 iterations. Customer segments were identified and analyzed based on their purchasing behaviors and demographic traits. For instance, Segment 0 preferred bulk purchases of lower-priced items, while Segment 1 favored higher-priced items in smaller quantities, resulting in a higher average purchase value of 2274.19. Conversely, Segment 2 showed a high frequency of returns, indicated by a negative average purchase value of -2608.40. Targeted pricing strategies were developed for each segment, aiming to maximize gross income. The effectiveness of the segmentation and pricing strategies was evaluated using metrics such as the silhouette score, with training and testing scores of 0.175 and 0.015 respectively, highlighting areas for improvement in clustering quality. This study underscores the potential of GMM in uncovering distinct customer segments and tailoring pricing strategies to enhance profitability. Future research should explore alternative clustering techniques and extend the analysis to other retail domains and larger datasets to validate and improve the findings. The practical implications for retail businesses include the need for iterative testing and refinement of pricing strategies based on customer segmentation to achieve sustainable growth and customer satisfaction.
Sentiment and Concern Classification on Metaverse Governance Responses Using Naïve Bayes and Support Vector Machine (SVM) Jalel Ben-Othman; Taqwa Hariguna
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.43

Abstract

The rapid advancement of immersive technologies such as the metaverse has introduced new opportunities and challenges for digital governance. Understanding public perception of these technologies is essential for designing governance systems that are transparent, inclusive, and responsive to citizens’ needs. This study analyses public sentiment and concerns regarding the use of metaverse technology in governance by applying two machine learning algorithms: Naïve Bayes and SVM. The dataset, consisting of open-ended survey responses from participants in The Gambia, was pre-processed through tokenization, stopword removal, and TF-IDF vectorization before model implementation. The results indicate that both algorithms can classify sentiment into positive, neutral, and negative categories; however, SVM consistently outperforms Naïve Bayes across all evaluation metrics. The SVM model achieved an accuracy of 88.6 percent and an F1-score of 0.873, demonstrating superior capability in recognizing contextual and semantic nuances within short text responses. In contrast, Naïve Bayes tended to overclassify responses as neutral, reflecting its limitation in capturing word dependencies. These findings confirm that SVM is better suited for sentiment analysis involving complex linguistic expressions and context-dependent opinions. The study contributes to the growing body of research on artificial intelligence in public policy by demonstrating how machine learning can provide deeper insights into citizen perspectives on emerging digital technologies. Such analytical approaches can assist policymakers in identifying public expectations, addressing concerns, and fostering trust in metaverse-based governance systems.
Co-Authors Jalel Ben-Othman Shih Chih Chen