SENTIMENT ANALYSIS ON TWITTER SOCIAL MEDIA ACCOUNTS: SHOPEECARE USING NAIVE BAYES, ADABOOST, AND SVM(EVOLUTION) ALGORITHM COMPARATIVE METHODS

  • Rizky Nugraha Pratama Universitas Nusa Mandiri https://orcid.org/0000-0002-3824-6634
  • Ghina Amanda Kamila
  • Kresna Lazani T
  • Ilham Fauzi
  • Muhammad Reynaldo Oktaviano
  • Dedi Dwi Saputra
DOI: https://doi.org/10.33480/techno.v19i1.3086

Abstract

The growth of Indonesian e-commerce is increasing along with the growth of internet use in Indonesia. In 2015, there were 92 million internet users in Indonesia. One of the popular online shopping platforms in Indonesia is Shopee. One of the services to see the response and reporting of problems from users, including shopeecare. shopeecare was created on the social media platform twitter to help facilitate communication between customers. with the amount of customer enthusiasm in tweeting and Retweeting existing content, we decided to research about Sentiment analysis on twitter social media accounts: Shopeecare uses the SMOTE NB, ADboost, and SVM comparison methods. From the data, the comparison results from the test experiments used the Smote + Naive Bayes, Smote + Naive Bayes + Adaboost, and Smote + SVM models. It is known that the Accuracy, Precision, AUC values of the Smote + SVM algorithm are higher than other algorithms, namely Accuracy 76.24%, Precision 75.65%, AUC 0.822. From the results of the algorithm comparison, it shows that the algorithm in determining the sentiment of the complaint and not complaint analysis is better than other algorithms.

Published
2022-07-04
How to Cite
Pratama, R., Kamila, G., T, K., Fauzi, I., Oktaviano, M., & Saputra, D. (2022). SENTIMENT ANALYSIS ON TWITTER SOCIAL MEDIA ACCOUNTS: SHOPEECARE USING NAIVE BAYES, ADABOOST, AND SVM(EVOLUTION) ALGORITHM COMPARATIVE METHODS. Jurnal Techno Nusa Mandiri, 19(1), 21 - 30. https://doi.org/10.33480/techno.v19i1.3086
Issue
Vol 19 No 1 (2022): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of March 2022
Section
Articles

Copyright (c) 2022 Rizky Nugraha Pratama, Ghina Amanda Kamila, Kresna Lazani T, Ilham Fauzi, Muhammad Reynaldo Oktaviano, Dedi Dwi Saputra

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license.

  1. The copyright in each article belongs to the author.
  2. Authors retain all their rights to published works, not limited to the rights set out on this page.
  3. The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC).
  4. Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri);
  5. The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author.
  6. If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.