Perbandingan Kinerja Algoritma Random Forest, KNN, dan SVM dalam Analisis Sentimen Cryptocurrency - AndaruJaya, Rinaldi Sukma; Suryono, Ryan Randy (Building of Informatics, Technology and Science (BITS), 2025)
Cryptocurrency is a digital money based on blockchain technology that offers security and transparency in transactions, so it has increasingly attracted the attention of the public, including in Indonesia. With the number of investors surpassing 20 million, cryptocurrencies have generated a variety of opinions on social media. Some see it as a promising modern investment opportunity, while others highlight the risks of price fluctuations, security, and unclear regulations. To understand public sentiment towards cryptocurrencies, machine learning-based sentiment analysis is a relevant solution. This research compares the performance of three popular algorithms, namely Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), in sentiment analysis of public opinion. These three algorithms have different advantages and disadvantages, depending on the characteristics of the data and the purpose of the analysis. Random Forest is known to be stable but requires high computation, KNN is easy to apply but less reliable on high-dimensional data, and SVM excels at separating complex data but requires careful parameter tuning. Previous research has shown differences in the accuracy of these three algorithms on various datasets, so further evaluation is needed to determine the most effective algorithm. The results of this study are expected to provide guidance in choosing the right algorithm for sentiment analysis, especially on cryptocurrency-related opinion data, as well as expand the understanding of the application of algorithms on dynamic and complex data.