Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS

Abstract

Sentiment analysis of YouTube boy group BTS comments uses the NLP approach to detect emotional patterns based on two category labels, namely positive and negative. With NLP, positive or negative polarity in an entity can be allocated as well as predicted high and low performance from various classification sentiments. The machine learning algorithms used to measure the accuracy of sentiment analysis developed are the Support Vector Machine and Random Forest algorithms. The steps taken start from the data collection obtained from the BTS YouTube Comment dataset and then go through the data preprocessing stage. Then proceed to the feature extraction stage by converting text into digital vectors or Bag of Words (BOW) and classified using machine learning algorithms until the evaluation stage. From the results comparison of the evaluated algorithms, the accuracy value between the two algorithms is 96% for training data and 85% for data testing using the SVM algorithm, while for the Random Forest algorithm it is 82% for training data and 80% for data testing. This shows that the SVM algorithm produces a higher accuracy value than the Random Forest for sentiment analysis of YouTube boy group BTS comments.