An Ensemble Boosting Approach with Boruta Feature Selection for Predicting E-Payment Adoption

Abstract: This study examined the factors influencing the adoption of electronic payment systems among Micro, Small, and Medium Enterprises (MSMEs) and developed a predictive model to evaluate the suitability of e-payment implementation. The research applied an ensemble machine learning approach consisting of AdaBoost, Binomial Boosting, L2 Boosting, GLM Boosting, and Random Forest to predict the likelihood of e-payment adoption. The novelty of this study lay in optimizing ensemble learning performance through Boruta-based feature selection, which improved the identification of the most relevant predictors. Data were collected from 1,500 MSME owners in DKI Jakarta, Indonesia, using a structured questionnaire. The Boruta feature selection process was implemented using predictor variables as input features and the adoption decision as the target variable, with maxRuns = 50, pValue = 0.05, mcAdj = TRUE, and getImpRfZ as the feature importance function. The GLM Boosting model was implemented using a binomial family for binary classification with a learning rate of 0.1 and a stopping iteration of 50. The results indicated that Perceived Risk, Perceived Usefulness, Subjective Norms, and Loyalty to E-payment Brands were the most influential factors affecting adoption. Among all models, GLM Boosting achieved the best performance with the highest test accuracy of 82.30%, demonstrating strong predictive capability and generalization performance. These findings provided practical insights for MSME owners and policymakers in designing strategies to improve e-payment adoption and supported the development of more effective digital financial inclusion policies.

Mariana Purba, Junaidi Junaidi, Lemi Iryani, Nia Umilizah, Handrie Noprisson and Nur Ani. “An Ensemble Boosting Approach with Boruta Feature Selection for Predicting E-Payment Adoption”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170441

@article{Purba2026,
title = {An Ensemble Boosting Approach with Boruta Feature Selection for Predicting E-Payment Adoption},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170441},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170441},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Mariana Purba and Junaidi Junaidi and Lemi Iryani and Nia Umilizah and Handrie Noprisson and Nur Ani}
}