Main Article Content

Abstract

Sentiment analysis is process to determine the sentiment of a person that is manifested in the form of text. Internet users write their opinions and everything that concerns them in the google play store review column. Moreover, when the world of education could not carry out face-to-face learning due to the covid-19 pandemic, learning turned to e-learning applications. Through this innovation, many pros and cons flow from the community with the existence of Ruangguru online learning application in the world of education. This research was conducted with the aim of determining word cloud visualization and the accuracy of the results of sentiment analysis of review data on the Ruangguru application using the C5.0 algorithm. The word cloud visualization results are dominated by word such as “paham”, “bagus”, “mudah”, “suka”, “langganan”, “seru”, “nyaman”, “senang”, “menarik”, “keren”, “lancar”, “sukses”. This shows that Ruangguru Application is a good application because it is dominated by positive sentiment words which means that users find it helpful and easy to understand the learning material in Ruangguru. The results of the Confusion Matrix show that the model successfully classifies 0.8557 or 85.57% of the data correctly from all test data

Keywords

Ruang Guru Sentiment Analysis Clssification C5.0 Algorithm

Article Details

How to Cite
Izzah, N., Nur’eni, & Pitri, R. (2023). SENTIMENT ANALYSIS OF REVIEW DATA OF THE RUANGGURU ONLINE LEARNING APPLICATION USING THE C5.0 ALGORITHM. Parameter: Journal of Statistics, 3(2), 76-83. https://doi.org/10.22487/27765660.2023.v3.i2.16919

References

  1. Albances, L. Z., Bungar, B. A., Patio, J. P., Sevilla, R. J. M., and Acula, D. (2018). Application of C5. 0 Algorithm to Flu Prediction Using Twitter Data. 2018 International Conference on Platform Technology and Service (PlatCon), 1-6.
  2. Ayani, D. D., Pratiwi, H. S., dan Muhardi, H. (2019). Implementation of Web Scraping for Data Retrieval on Marketplace Sites. Journal of Information Systems and Technology (JUSTIN), 7(4), 257.
  3. Balamurugan, M., dan Kannan, S. (2016). Performance Analysis of Cart and C5.0 using Sampling Techniques. International Conference on Advances in Computer Applications (ICACA), 72-75.
  4. Dalbergio, D., Hayati, M. N., dan Nasution, Y. N. (2019). Classification of Student Length of Study Using the C5.0 Method in Case Studies of Student Graduation Data from the Faculty of Mathematics and Natural Sciences, Mulawarman University in 2017. Pros. Semin. Nas. Mat. Stat. dan Apl, 1(1), 36–42.
  5. Dharmawan, L.R., Arwani, I., dan Ratnawati, D.E. (2020). “Sentiment Analysis on Twitter Social Media on Brawijaya University Student Academic Information System Services using the KNearest Neighbor Method”. Journal of Information Technology and Computer Science Development, 4(3), 959–965.
  6. Databoks. (2022). Survey: Ruangguru is the most popular educational startup in Indonesia. Retrivied from https://databoks.katadata.co.id/.
  7. Listyorini, T., dan Widodo, A. (2013). Designing Mobile Learning for Android-Based Operating System Courses. Symmetric J. Tech. Mechanical, Electrical and Computer Science, 3(1), 25.
  8. Ruangguru. (2020). About Ruangguru. Retrivied from https://www.ruangguru.com/about/.
  9. Wahyono, H. (2019). Utilization of Information Technology in Assessing Learning Outcomes of the Millennial Generation in the Era of Industrial Revolution 4.0. Proceeding Biol. Educ., 3(1), 192–201.