Analisis Sentimen Kepuasan Publik terhadap Pembaruan Alun-Alun Jember Menggunakan Metode Random Forest
DOI:
https://doi.org/10.55606/juitik.v6i2.2445Keywords:
Alun-Alun Jember, Analisis Sentimen, K-Fold Cross Validation, Random Forest, SMOTEAbstract
The renovation of Jember Alun-Alun has sparked a variety of responses from the public, many of which have been expressed on social media, necessitating an automated method to efficiently analyze these public opinions. This study aims to analyze public sentiment toward the renovation of Jember Alun-Alun using the Random Forest algorithm and to identify the distribution of sentiment into three categories: positive, negative, and neutral. Data was collected via web scraping techniques using APIs from the X and TikTok platforms with the keywords #jembernusantara and #alunalunjember during the period from June 6, 2024, to February 20, 2025, yielding a total of 1,420 comments, which were then processed through the stages of cleaning, case folding, tokenization, normalization, stopword removal, stemming, and feature weighting using TF-IDF, as well as class balancing with SMOTE and model evaluation using K-Fold Cross Validation (K=2 to K=10). The test results showed that the Random Forest model was able to classify sentiment with an accuracy ranging from 89% to 96%, with the highest accuracy of 96% at K=6, K=8, and K=10, and the best F1-Score for the negative class reaching 100%. It is therefore concluded that the combination of the Random Forest and SMOTE methods is effective for analyzing public sentiment in social media text data.
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