Perbandingan Algoritma Random Forest, KNN, SVM Untuk Analisis Sentimen Pengalaman Belanja Thrift Di X
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M Rafi Raihandika
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
- Ryan Randy Suryono Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia https://orcid.org/0000-0001-9378-8148 (*) Corresponding Author
Abstract
The thrifting phenomenon is gaining traction, especially among millennials and Generation Z. Along with the increasing interest in thrifting, X social media has emerged as one of the main platforms for people to share experiences and opinions related to thrift shopping. This research aims to analyze people's sentiments about thrift shopping experiences by comparing the performance of Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. The dataset used in this study was obtained from Twitter as many as 6,390 tweets collected through crawling techniques with a time span of August 2, 2024 to September 4, 2024. The dataset is then processed to produce clean data. After the cleaning process, the data is divided 80:20 for training and testing. In testing the three algorithms, an accuracy level is obtained that shows how well the model makes predictions. This accuracy measures the extent to which the model successfully predicts the sentiment of the thrifting shopping experience based on the Twitter dataset. The results show that the Random Forest algorithm has the highest accuracy with 95%, precision 97%, recall 78%, and f1-score 85%. SVM achieved 93% accuracy, 93% precision, 72% recall, and 78% f1-score. KNN obtained 89% accuracy, 72% precision, 59% recall, and 61% f1-score. From the results obtained, the Random Forest algorithm shows the best accuracy for sentiment analysis of thrifting experiences on Twitter Indonesia. Its advantage lies in its stable ensemble learning approach, where multiple decision trees are combined to produce more accurate predictions. This ability makes Random Forest effective in handling varied and complex Twitter text data, making it the most reliable algorithm in this context.
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