Analisis Sentimen Traveloka Berdasarkan Ulasan Google Play Store Menggunakan Algoritma Support Vector Machine dan Random Forest


  • Siti Rohimah * Mail Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • M Afdal Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Mustakim Mustakim Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • (*) Corresponding Author
Keywords: Confusion Matrix; K-Fold Cross Validation; Random Forest; Sentiment Analysis; Support Vector Machine; Traveloka

Abstract

The internet has become a key element in supporting technological and information advances in various sectors of human activity. In the trade and tourism sector, the Traveloka application is the favorite choice of Indonesian people. Reviews or reviews from users play an important role for the Company to understand the level of customer satisfaction. However, currently there are several users who give high ratings but contain negative reviews. Based on these problems, this research aims to understand more deeply user opinions, so that they can be used to improve services and features as well as test and compare the accuracy of the two algorithms in classifying user sentiment. In this research, the Support Vector Machine and Random Forest classification methods were used. The research results show that Random Forest has superior and stable performance compared to SVM, with higher average accuracy for most features, such as Traveloka (71% & 67%) and Airplanes (75% & 74%). Evaluation with k-fold cross validation supports these results, with higher average Random Forest accuracy on features such as Traveloka (70% & 66%) and Airplanes (75% & 74%).

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Article History
Submitted: 2024-11-17
Published: 2024-12-18
Abstract View: 84 times
PDF Download: 85 times
How to Cite
Rohimah, S., Afdal, M., Mustakim, M., & Novita, R. (2024). Analisis Sentimen Traveloka Berdasarkan Ulasan Google Play Store Menggunakan Algoritma Support Vector Machine dan Random Forest. Building of Informatics, Technology and Science (BITS), 6(3), 1709-1716. https://doi.org/10.47065/bits.v6i3.6300
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