CYBERBULLYING SENTIMENT ANALYSIS OF INSTAGRAM COMMENTS USING NAÏVE BAYES CLASSIFIER AND K-NEAREST NEIGHBOR ALGORITHM METHODS

Fitri Anisa Nirmala; Muhammad Jazman; Nesdi Evrilyan Rozanda; Febi Nur Salisah

doi:10.52436/1.jutif.2024.5.5.1997

Fitri Anisa Nirmala Information Systems, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Muhammad Jazman Information Systems, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Nesdi Evrilyan Rozanda Information Systems, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Febi Nur Salisah Information Systems, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.1997

Keywords: Cyberbullying K-Nearest Neighbor, Naïve Bayes Classifier, Sentiment Analysis

Abstract

The high number of social media users presents major threats and risks, such as cyberbullying Cyberbullying or cyberbullying is one of the negative impacts of the rapid development of technology and social media. Sentiment Analysis is a technique for extracting text data to obtain information about positive, neutral or negative sentiment. One of Indonesian social media that often gets user sentiment through social media is Instagram. By using the Text Mining technique, the classification method will determine whether a sentiment is positive, neutral or negative. This research uses the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) methods with tf-idf weighting accompanied by the addition of an emotional icon (emoticon) conversion feature to determine the existing sentiment classes from tweets about Instagram users. The results of calculations using the first three methods using the Partitionong model, the results using the Naive Bayes method, get an accuracy value of 91.25%, a recall value of 92% and a precision value of 90% and calculations using the KNN method have an accuracy value of 67%, a recall value of 49% and a precision value of 34 %. So it can be concluded that the Naïve Bayes Classifier algorithm has the best performance.

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