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Elveny, Maricha
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Enhanced Detection of Consumer Behavioral Shifts in E-Commerce Platforms with Transformer-Based Algorithms Syah, Rahmad B.Y; Elveny, Maricha; Darmansyah, Soleh; Silviana, Lia
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.907

Abstract

This research aims to analyze changes in consumer behavior on e-commerce platforms using consumer interaction data such as view, add to cart, and purchase.  Identifying changes in consumer behavior on e-commerce platforms is very important because it can provide deeper insight into consumer motivations and preferences. By better understanding how consumers interact with products, companies can design more targeted strategies to increase conversions, reduce cart abandonment, and improve the overall customer experience. The DistilBERT based prediction model is applied to detect and predict changing patterns of consumer behavior in the purchasing process. DistilBERT was chosen because of its more efficient capabilities compared to previous models which enable faster data processing and lower resource usage, which is very important for real-time applications on e-commerce platforms with big data. The data used includes consumer interactions during a certain period, with model evaluation using precision, recall, F1-score, and accuracy metrics. The results showed that despite an increase in the number of actions such as View and Add to Cart, conversion to Purchase was still hampered, indicating a cart abandonment problem. The model used managed to achieve 90% accuracy, with a precision value of 0.87, recall of 0.85, and F1-score of 0.86, showing excellent performance in predicting changes in consumer behavior. Based on the results of this analysis, companies can optimize marketing strategies by targeting consumers who have added products to their basket but have not yet made a purchase, as well as making price adjustments, discounts, and limited time offers. This research also emphasizes the importance of using real-time data to dynamically adjust marketing strategies and improve customer experience.
Predicting Burnout in Start-Up Environments: A Multivariate Risk Scoring Approach for Early Managerial Intervention Sutrisno, Nos; Elveny, Maricha; Lubis, Andre Hasudungan; Syah, Rahmad; Hartono, Hartono; Krisdayanti, Sabina
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1663

Abstract

Start-up organisations operate under fast timelines, lean staffing, and constantly shifting priorities, exposing employees to chronic workload pressure and emotional strain. Unmanaged burnout in these settings threatens individual well-being, talent retention, and long-term execution capacity. This study proposes a multivariate burnout risk scoring approach that aims to identify and prioritise employees at elevated risk before full deterioration occurs, enabling early managerial intervention rather than reactive recovery. The proposed pipeline integrates principal component analysis (PCA), Random Forest, and Support Vector Machine (SVM). PCA is first applied to reduce redundancy across workplace indicators, yielding five principal components (PC1–PC5) that together explain 88% of the total variance in self-reported stress level, job satisfaction, emotional exhaustion, work-life balance, performance, and social interaction. These components are then used as predictors in two supervised classification models, Random Forest and SVM, to estimate the likelihood that each employee belongs to a high-burnout-risk class. The Random Forest model achieved an accuracy of 88%, and the SVM model achieved an accuracy of 86%, demonstrating strong predictive capability in distinguishing higher-risk employees from lower-risk employees. The resulting predicted probability is interpreted as an individualised burnout risk score, which can be mapped to action categories such as workload redistribution, role clarification, targeted supervisory check-ins, or temporary protection from critical-path tasks. In this way, the framework operationalises burnout prediction not only as a detection task but also as an actionable decision-support signal for leaders. The study therefore offers both a quantitative method for forecasting burnout in start-up environments and a practical structure for translating prediction into preventive intervention.
Co-Authors Andre Hasudungan Lubis Darmansyah, Soleh Hartono Hartono Krisdayanti, Sabina Rahmad Syah, Rahmad Silviana, Lia Sutrisno, Nos Syah, Rahmad B.Y