Application of Support Vector Machine (SVM) Algorithm in Analyzing Public Sentiment in the Media  Tiktok Related to Tour de Entete Event

Frich Nandra Mangi; Fajar Hariadi; Leonard  Marten Doni Ratu

doi:10.59934/jaiea.v5i2.2134

Authors

Frich Nandra Mangi Universitas Kristen Wira Wacana Sumba
Fajar Hariadi Universitas Kristen Wira Wacana Sumba
Leonard Marten Doni Ratu Universitas Kristen Wira Wacana Sumba

DOI:

https://doi.org/10.59934/jaiea.v5i2.2134

Keywords:

Sentiment Analysis, Support Vector Machine, Tour De EnTeTe, Sport Tourism, Social Media, TF-IDF.

Abstract

This study aims to analyze public sentiment on TikTok social media towards the implementation of the Tour De EnTeTe event using the Support Vector Machine (SVM) algorithm. The phenomenon of using social media as a space for public opinion provides an opportunity for local governments to evaluate the psychological and social impact of sports tourism activities in real-time. The research data was obtained through a crawling technique that produced 1,000 data entries, which were then reduced to 808 valid data after going through the text preprocessing stage. The analysis is done by classifying the data into positive and negative sentiments. The results showed that the SVM model was able to provide excellent performance with an accuracy rate of 89%, precision of 91%, and recall of 97%. The findings show a significant dominance of positive sentiment, reflecting the high enthusiasm and support of the public for the Tour De EnTeTe. Although there are constraints on language ambiguity that lead to some misclassification, overall the SVM algorithm has proven to be effective and accurate as an instrument for digital opinion analysis. This study concludes that the public perception of the event is very positive, so that it can be used as a strategic reference in the development of tourism promotion in East Nusa Tenggara in the future.

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