Prediction of Customer Engagement Response to E-wallet Content Based on Machine Learning Using Combined E-Wallet Dataset and Individual E Wallet Dataset

Aug 4, 2026

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Research addresses how e-wallet providers should strengthen marketing after QRIS standardization intensified competition in Indonesia. The study predicts tweet engagement types on Twitter using machine learning, improving on prior work that relied mainly on interviews and traditional statistical approaches. Experiments use 15,756 directly collected tweets and models including XGBoost, Random Forest, Decision Tree, and KNN. Results show XGBoost and KNN perform best, with similar prediction outcomes between combined and individual e-wallet datasets, while future work requires larger, more complex data for deeper learning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Methodological Gap

Methodology

Data Collection from Twitter

Machine Learning Models

Findings and Results

Best-Performing Algorithms

Comparison of Combined vs Individual Datasets

Conclusion

Limitations and Future Research

MIX: Jurnal Ilmiah Manajemen Management Scientific Journal ISSN (Online): 2460-5328, ISSN (Print): 2088-1231 [https://publikasi.mercubuana.ac.id/index.php/jurnal_Mix](https://publikasi.mercubuana.ac.id/index.php/jurnal_Mix) Prediction of Customer Engagement Response to E-wallet Content Based on Machine Learning Using Combined E-Wallet Dataset and Individual E Wallet Dataset Muhammad Ibnu Rizaldi1) ; Sunu Widianto2) 1*) [muhammad19179@mail.unpad.ac.id](muhammad19179@mail.unpad.ac.id), Padjadjaran University, Indonesia 2) [sunu.widianto@mail.unpad.ac.id](sunu.widianto@mail.unpad.ac.id), Padjadjaran University, Indonesia *) Corresponding Author | ABSTRACT | | --- | | Objectives: The COVID pandemic proved that humans are not hesitant to adopt technology in the banking industry, especially as a method of payment. In Indonesia, this fact is demonstrated by the value of e-wallet transactions in 2021 which reached 18,5 billion USD. The implementation of QRIS (Quick Response Code Indonesian Standard) in the payment systems of e-wallets compels every e-wallet provider to intensify their marketing activities to overcome competitors. Previous studies on this topic primarily focused on factors that influence customer engagement on social media using traditional approaches, such as interviews and statistical methods to process the data. This research aims to overcome the limitations of previous research by using machine learning methods in predicting the customer engagement type of primary data collected directly from social media in this case Twitter.
Methodology: In this paper, we propose the application of machine learning methods such as XGBoost, Random Forest, Decision Tree, and KNN to predict the most likely engagement type of a tweet related to e-wallet content using 15.756 data which are directly collected from Twitter.
Finding: This research successfully found that XGBoost and KNN are the machine learning algorithms that perform best and the results in prediction from using the combined dataset and the individual e-wallet brands dataset are similar.
Conclusion: Even though the prediction accuracy in this research is good, this research still has many limitations. Thus, future research in the same field would benefit from a larger amount of data to accommodate machine learning algorithms that are more complex like deep learning.
Keywords: E-Wallet; Machine Learning; Customer Engagement; Marketing | | Submitted: Revised: Accepted: | 2023-09-26 2024-02-08 2024-02-21 Article Doi: [http://dx.doi.org/10.22441/jurnal_mix.2024.v14i1.012](http://dx.doi.org/10.22441/jurnal_mix.2024.v14i1.012) [http://dx.doi.org/10.22441/jurnal_mix.2024.v14i1.012](http://dx.doi.org/10.22441/jurnal_mix.2024.v14i1.012) 213 MIX: Jurnal Ilmiah Manajemen Volume 14 Number 1 | February 2024 p-ISSN: 2088-1231 e-ISSN: 2460-5328 INTRODUCTION The adoption of technology in the banking industry especially as a method of payment is a common thing nowadays. The global financial technology industries didn't stop growing when the pandemic hit (Widokarti et al., 2022) . In Indonesia, the total e-wallet transaction reached 18,5 billion USD in 2021 which is a 32% Year on Year growth compared to 2020 (Indonesia E-Wallet Transaction to Reach $18.5 Billion in 2021 amid Fierce Competition- The Asian Banker, n.d.) . This is in line with the attempt of Bank Indonesia’s effort to increase cashless transactions to reduce the cost of printing money (Yuliastuti et al., 2022) . The widespread use of e-wallets in everyday life is further supported by the emergence of multiple e-wallet platforms in Indonesia. These platforms allow people to make payments simply by using a mobile application on their smartphones. QRIS (Quick Response Code Indonesian Standard) is a QR Code standard that was launched on August 17th, 2019 by the Bank of Indonesia and the Indonesian Payment System Association. It aims to standardize cashless payments in Indone

FAQs

What problem does the research focus on regarding e-wallet marketing?

It focuses on predicting customer engagement responses to e-wallet marketing content after QRIS standardization makes differentiation harder and competition stronger.

Which machine learning algorithms are used in the study?

The study applies XGBoost, Random Forest, Decision Tree, and KNN to predict the engagement type of tweets related to e-wallet content.

How much data is collected and from where?

The research uses 15,756 tweets collected directly from Twitter as primary data.

What do the findings indicate about the best-performing models?

XGBoost and KNN deliver the best performance, and prediction results using the combined dataset are similar to those using individual e-wallet brand datasets.

Aug 4, 2026

Upload by Maeve