Prediction of payment method in convenience stores using machine learning - Pratondo, Agus; Novianty, Astri; Pudjoatmodjo, Bambang (2023)
Predicting payment modes is a critical aspect of financial analysis and planning, with implications for various industries, including banking, e-commerce, and market research. However, the lack of accurate and robust predictive models for determining payment modes poses a significant challenge in optimizing financial strategies and decision-making processes across industries such as banking, e-commerce, and market research. This study explores the application of machine learning techniques, specifically the Random Forest algorithm, to predict payment modes in the context of the Indonesian community. The dataset used in this study was collected from a diverse sample of the Indonesian population, reflecting the multifaceted nature of payment behaviors in the region. The Random Forest algorithm was employed due to its robustness in handling complex, high-dimensional data, and its ability to provide reliable predictions. Leveraging a carefully curated set of feature attributes, our model achieved an impressive accuracy rate of 98% in predicting payment modes. The findings of this research have practical implications for businesses and financial institutions operating in Indonesia. The high accuracy rate suggests that machine learning models can effectively assist in tailoring services and marketing strategies based on predicted payment preferences.