Article Per Year (5 Year)
p-Index From 2020 - 2025
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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Digital Market and Digital Currency
Ahmad, Asmala bin
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Interpretable Product Recommendation through Association Rule Mining: An Apriori-Based Analysis on Retail Transaction Data Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala bin
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.v8i2.252

Abstract

The rapid growth of e-commerce has generated vast amounts of transactional data, creating opportunities for data-driven decision-making in retail environments. This study presents an interpretable product recommendation approach based on association rule mining using the Apriori algorithm. Unlike complex black-box recommender models, the proposed method emphasizes transparency and explainability in identifying purchasing relationships. The Groceries dataset comprising 38,765 transactions was analyzed to discover frequent itemsets and generate actionable association rules. After applying minimum thresholds of 0.02 for support and 0.4 for confidence, a total of 67 frequent itemsets and 45 strong rules were obtained. The rule {whole milk, sausage, rolls/buns} → {yogurt} achieved the highest lift value of 1.66, revealing meaningful co-purchasing behavior. Visualization tools, including heatmaps and network graphs, were employed to illustrate rule strength and product interconnections, facilitating business interpretation. The findings demonstrate that interpretable rule-based recommendations can effectively support product bundling, cross-selling, and retail layout strategies. This study highlights the continuing relevance of Apriori in creating transparent, data-driven insights and proposes future integration with hybrid models to address personalization and scalability challenges in modern recommendation systems.
Predicting Customer Conversion in Digital Marketing: Analyzing the Impact of Engagement Metrics Using Logistic Regression, Decision Trees, and Random Forests Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala bin
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.34

Abstract

This research explores the impact of engagement metrics on predicting customer conversion rates within digital marketing, employing three advanced predictive modeling techniques: Logistic Regression, Decision Trees, and Random Forests. Using a comprehensive dataset of 8,000 customer interactions, the study evaluates critical engagement metrics such as PagesPerVisit, TimeOnSite, and EmailClicks to determine their influence on conversion outcomes. The results indicate that PagesPerVisit and TimeOnSite are the most significant predictors of customer conversion, with the Random Forest model outperforming others, achieving an accuracy of 87.1% and an ROC-AUC score of 0.6979. The Logistic Regression model demonstrated the highest recall for the conversion class at 99.8%, but its performance in predicting non-conversions was less robust, highlighting the challenges of imbalanced datasets. Decision Trees, while offering valuable interpretability, showed a lower accuracy of 79.6% and struggled with precision in identifying non-conversions. These findings suggest that enhancing on-site customer engagement and refining email marketing strategies are pivotal for improving conversion rates. The study contributes to the field of digital marketing analytics by providing empirical evidence on the relative importance of various engagement metrics and offering practical insights for optimizing digital marketing strategies. Additionally, it highlights the benefits of using ensemble methods like Random Forests to achieve more balanced and accurate predictions in customer conversion scenarios.
Co-Authors Aboobaider, Burhanuddin bin Mohd Prasetio, Agung Budi