Sentiment Analysis on the Shopee Application on Playstore Using the Random Forest Classification Method

  • Muhammad Rusdi Rahman Informatics Department, Universitas Amikom Yogyakarta
  • Ahmad Febri Diansyah Informatics Department, Universitas Amikom Yogyakarta
  • Hanafi Hanafi Informatics Department, Universitas Amikom Yogyakarta
https://doi.org/10.25139/inform.v9i1.5465
Abstract views: 545 , PDF downloads: 447
Keywords: Sentiment Analysis, Shopee, TF-IDF, Random Forest, Play Store

Abstract

In analyzing customer or consumer satisfaction with company services, it is essential for companies to find service deficiencies and to know user expectations for the company. This study aims to build sentiment analysis on the Shoppe application on the Google Playstore. The method used includes TF-IDF as text vectorization, Random Forest as a classification model, and Evaluation Matrix as an evaluation model, providing accuracy, precision, recall, and F1-Score. Based on the results of this study, the model we used achieved an accuracy rate of 94%, a precision of 91%, a recall of 91%, and an F1-Score of 93%. The limitations of this study are identifying words in English and regional languages because the corpus module we use is literature, a special Indonesian corpus. In future research, we will try to build a new engine/algorithm and try to add datasets in the hope that the level of accuracy will be even better.

Author Biography

Hanafi Hanafi, Informatics Department, Universitas Amikom Yogyakarta

 

 

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Published
2023-11-29
How to Cite
Rusdi Rahman, M., Febri Diansyah, A., & Hanafi, H. (2023). Sentiment Analysis on the Shopee Application on Playstore Using the Random Forest Classification Method. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 9(1), 20-24. https://doi.org/10.25139/inform.v9i1.5465
Issue
Vol. 9 No. 1 (2024)
Section
Articles

Copyright (c) 2023 Muhammad Rusdi Rahman, Ahmad Febri Diansyah, Hanafi Hanafi

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