Analysis Of Public Sentiment Towards Naturalized Players In The Indonesian National Team Using The Naïve Bayes Method

T. Raihan Yudisthira; Abdul Halim Hasugian

doi:10.34288/jri.v7i4.398

Authors

T. Raihan Yudisthira Universitas Islam Negeri Sumatera Utara
Abdul Halim Hasugian Universitas Islam Negeri Sumatera Utara

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v7i4.398

Keywords:

Naïve Bayes, Naturalization, Sentiment, Confusion Matrix

Abstract

The increasing number of naturalized Indonesian national team players in the Garuda squad has triggered various reactions and opinions among the public, both pro and con. This study aims to identify and classify these sentiments, whether positive, negative, or neutral. The method used in this study is to use Naive Bayes because of its excellent ability to classify text based on the probability of word occurrence. In order to obtain more accurate results, several preprocessing stages need to be carried out through several steps, namely cleaning, case folding, normalization, stopword removal, tokenizing, and stemming on the data to be processed for maximum results from each stage. The results of the study showed that the majority of public sentiment tends to be more neutral towards the contribution of naturalized Indonesian national team players. To determine the percentage of results from the specified classification, a Confusion Matrix will be used. The results of the classification process using the Naive Bayes method produce data into 3 types, namely 33 positive classes, 357 neutral classes, and 13 negative classes with an accuracy value of 89%, precision 63%, recall 34%, and f1-score 33%. This sentiment analysis provides an overview of public comments regarding the presence of naturalized Indonesian national team players regarding public acceptance of the naturalization policy and can be input for PSSI in making decisions regarding the development of the national team in the future in order to improve the quality of the national team in the future

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