Prediction of payment method in convenience stores using machine learning
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 op...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2023
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/28103/1/Prediction%20of%20payment%20method%20in%20convenience%20stores%20using%20machine%20learning.pdf http://eprints.utem.edu.my/id/eprint/28103/ https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10419978 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832718831795044352 |
|---|---|
| author | Pratondo, Agus Novianty, Astri Pudjoatmodjo, Bambang |
| author_facet | Pratondo, Agus Novianty, Astri Pudjoatmodjo, Bambang |
| author_sort | Pratondo, Agus |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Conference or Workshop Item |
| id | my.utem.eprints-28103 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2023 |
| record_format | eprints |
| spelling | my.utem.eprints-281032024-10-17T16:30:35Z http://eprints.utem.edu.my/id/eprint/28103/ Prediction of payment method in convenience stores using machine learning Pratondo, Agus Novianty, Astri Pudjoatmodjo, Bambang 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. 2023 Conference or Workshop Item PeerReviewed text en http://eprints.utem.edu.my/id/eprint/28103/1/Prediction%20of%20payment%20method%20in%20convenience%20stores%20using%20machine%20learning.pdf Pratondo, Agus and Novianty, Astri and Pudjoatmodjo, Bambang (2023) Prediction of payment method in convenience stores using machine learning. In: 11th IEEE Conference on Systems, Process and Control, ICSPC 2023, 16 December 2023, Malacca. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10419978 |
| spellingShingle | Pratondo, Agus Novianty, Astri Pudjoatmodjo, Bambang Prediction of payment method in convenience stores using machine learning |
| title | Prediction of payment method in convenience stores using machine learning |
| title_full | Prediction of payment method in convenience stores using machine learning |
| title_fullStr | Prediction of payment method in convenience stores using machine learning |
| title_full_unstemmed | Prediction of payment method in convenience stores using machine learning |
| title_short | Prediction of payment method in convenience stores using machine learning |
| title_sort | prediction of payment method in convenience stores using machine learning |
| url | http://eprints.utem.edu.my/id/eprint/28103/1/Prediction%20of%20payment%20method%20in%20convenience%20stores%20using%20machine%20learning.pdf http://eprints.utem.edu.my/id/eprint/28103/ https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10419978 |
| url_provider | http://eprints.utem.edu.my/ |