IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK MENGANALISIS SENTIMEN USER TWITTER

Abstract

Twitter is a frequently used social media that is used to state opinions whether it is positive or negative. The purpose of this research is to analyze the sentiment of Twitter users regarding an issue. The case study for this research is the incident of Tol Cikampek. The data that is proper to use for sentiment analysis are 618 data tweets which consists of 237 positive data tweets and 389 negative data tweets taken during December-January 2021 using Twint application in Python. Data tweets that are taken goes through pre-processing stage which consists of case folding, data cleansing, tokenization, normalization, stopword removal, and stemming. After pre-processing, data weighting is done using Term Frequency-Inverse Document Frequency (TF-IDF) and classification is done using the method of K-Nearest Neighbor with cosine similarity to calculate the distance between documents. Based on the evaluation results using confusion matrix, the highest accuracy is 83,1% when k=9, the highest precision 2 is 66,7% when k=5 and the highest recall is 87,5% when k=9.