Vol.12, No.4, November 2023.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Sentiment Analysis Model Development on E-Money Service Complaints


Imam Tahyudin, Andhika Rafi Hananto, Silvia Anggun Rahayu, Rayinda Maya Anjani, Ade Nurhopipah


© 2023 Imam Tahyudin, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 12, Issue 4, Pages 2050-2055, ISSN 2217-8309, DOI: 10.18421/TEM124-15, November 2023.


Received: 27 May 2023.

Revised:   16 August 2023.
Accepted: 04 September 2023.
Published: 27 November 2023.




Technology provides various conveniences for users in many aspects, such as in the commercial business world with the development of financial technology or fintech. In Indonesia, there are fintech services with the most users, namely OVO and DANA. Various kinds of criticisms and suggestions related to deficiencies and weaknesses in OVO and DANA services were submitted by users via social media Twitter. This study aims to analyze and develop a sentiment identification model for user complaints against OVO and DANA services on Twitter. This study uses the four classification algorithm methods: Random Forest, K-NN, Decision Tree, and Naïve Bayes Classifier. The data from social media Twitter is taken from user tweets regarding criticism and complaints on OVO and DANA services. The data obtained was 5,000 instances consisting of 20% positive and 80% negative reviews. Accuracy values represent the study results with 87% for Naive Bayes, 86% for Decision Tree, 91% for K-NN, and 87% for Random Forest. Based on these results, it can be concluded that testing of sentiment analysis on user complaints on OVO and DANA services using the K-NN algorithm is superior to the other algorithms. This research results in a system that combines the four prediction models. The evaluation shows that the developed model can accurately classify user sentiment.


Keywords –Sentiment analysis, K-NN, naïve Bayes, decision tree, random forest.



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