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All Journal IJIIS: International Journal of Informatics and Information Systems Proceeding of International Conference Health, Science And Technology (ICOHETECH) Journal of Digital Market and Digital Currency International Journal Research on Metaverse
Aboobaider, Burhanuddin bin Mohd
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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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.
Intelligent Surveillance for Mask Regulation in Healthcare Using the YOLOv11 Algorithm Pradana, Afu Ichsan; Harsanto; Aboobaider, Burhanuddin Bin Mohd; Harsanto, Malika
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2025: Proceeding of the 6th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/23mc9656

Abstract

The use of face masks in healthcare settings is a crucial measure in preventing the spread of infectious diseases, particularly since the outbreak of the COVID-19 pandemic. However, public compliance with mask-wearing remains a challenge despite the implementation of various regulations. This study aims to design and develop an automatic mask-wearing detection system by leveraging the YOLOv11 algorithm, which is renowned for its superior speed and accuracy in object detection. The methodology involved collecting a dataset of facial images with and without masks, data labeling, model training using YOLOv11, and evaluating the system's performance in real-world conditions. Test results demonstrate that the system can perform real-time mask detection with a mean Average Precision (mAP) of 0.9, establishing it as an effective solution for supporting health protocol monitoring in medical facilities. Consequently, this system not only enhances monitoring efficiency but also has the potential to minimize the risk of infection spread through an intelligent technological approach.
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.
Scam Detection in Metaverse Platforms Through Advanced Machine Learning Techniques Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.19

Abstract

The rapid expansion of metaverse environments has introduced novel opportunities and challenges, particularly concerning user security and trust. This study investigates the application of machine learning techniques to detect scam activities within the metaverse by analyzing user behaviors and interaction patterns. Using a comprehensive dataset, we evaluated three machine learning models—Random Forest, Support Vector Machine (SVM), and Neural Network—for their effectiveness in identifying scams. The Neural Network model achieved the highest performance, with an accuracy of 91%, a recall of 92%, and an AUC of 95%, making it the most reliable solution for this task. Feature importance analysis revealed that attributes such as the number of transactions and average transaction value significantly contribute to scam detection. Hyperparameter optimization further improved model performance, demonstrating the potential of fine-tuned architectures in handling high-dimensional datasets. Despite the Neural Network’s superior performance, its computational complexity highlights the need for lightweight implementations for real-time applications. This research contributes to the growing field of metaverse security by providing a robust framework for scam detection using machine learning. Future work should focus on expanding datasets to capture multi-platform behaviors, incorporating explainable AI (XAI) for improved interpretability, and enhancing model efficiency. These advancements will ensure safer and more trustworthy metaverse ecosystems for users worldwide.
Co-Authors Ahmad, Asmala Ahmad, Asmala bin Harsanto Harsanto, Malika Pradana, Afu Ichsan Prasetio, Agung Budi