UNBALANCED MULTICLASS CLASSIFICATION WITH ADAPTIVE SYNTHETIC MULTINOMIAL NAIVE BAYES APPROACH

Abstract

Opinions related to rising fuel prices need to be seen and analysed. Public opinion is closely related to public policy in Indonesia in the future. Twitter is one of the media that people use to convey their opinions. This study uses sentiment analysis to look at this phenomenon. Sentiment is divided into three categories: positive, neutral, and negative. The methods used in this research are Adaptive Synthetic Multinomial Naive Bayes, Adaptive Synthetic k-nearest neighbours, and Adaptive Synthetic Random Forest. The Adaptive Synthetic method is used to handle unbalanced data. The data used in this study are public arguments per province in Indonesia. The results obtained in this study are negative sentiments that dominate all provinces in Indonesia. There is a relationship between negative sentiment and the level of education, internet use, and the human development index. Adaptive Synthetic Multinomial Naive Bayes performed better than other methods, with an accuracy of 0.882. The highest accuracy of the Adaptive Synthetic Multinomial Naive Bayes method is 0.990 in Papua Barat Province.

Keywords

• adaptive synthetic
• classification
• imbalance data
• accuracy