Comparison of Random Forest and Support Vector Machine Learning Algorithms in Sentiment Analysis of Gojek User Reviews

Tesa Vausia Sandiva; Arip Kristiyanto

doi:10.35134/komtekinfo.v12i4.669

Authors

Tesa Vausia Sandiva Universitas Pamulang
Arip Kristiyanto Universitas Pamulang https://orcid.org/0000-0002-1528-1971

DOI:

https://doi.org/10.35134/komtekinfo.v12i4.669

Keywords:

Sentiment analysis, Go-jek, Random forest, SVM, Machine Learning

Abstract

The development of digital technology has brought significant changes across various sectors of life, including transportation. One of the most popular modes of transportation among the public today is online motorcycle taxis, such as Gojek. Gojek continues to innovate to meet customer needs more effectively and to expand its range of services. This study aims to identify the number of positive, neutral, and negative sentiments in a user review dataset, as well as to evaluate the performance of the algorithms used—namely, SVM and Random Forest. The analysis was conducted on 10,000 customer reviews from the Play Store application, resulting in 2,057 positive sentiments, 1,135 neutral sentiments, and 6,295 negative sentiments. The classification model compared the SVM algorithm with the Random Forest algorithm, and the results show that Random Forest achieved better performance, with 91% accuracy compared to SVM’s 89%. These findings demonstrate that Random Forest performs better in handling word distribution within review texts than the SVM method.

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