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

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.

@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},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mariana Purba and Junaidi Junaidi and Lemi Iryani and Nia Umilizah and Handrie Noprisson and Nur Ani},
  doi       = {10.14569/IJACSA.2026.0170441},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170441}
}