Coastal Sentiment Review Using Naïve Bayes with Feature Selection Genetic Algorithm
(1) Department of Informatics, StatePolytechnic of Cilacap, Indonesia
(2) Department of Informatics, Politeknik Negeri Cilacap, Indonesia
(3) Department of Informatics, StatePolytechnic of Cilacap, Indonesia
Abstract
Purpose: The tourism potential in the maritime sector can be Indonesia's mainstay at this time, especially in enjoying the charm of the natural beauty of the coast as people know Indonesia is an archipelagic country. The purpose of this study is to find the best model by applying the feature selection genetic algorithm (GA) and Information Gain (IG) to get the best Naïve Bayes (NB) model and the best features to produce the best level of sentiment classification accuracy.
Methods: The stages of the research were carried out by going through the process of searching, pre-processing, analyzing research data using the Naïve Bayes model and optimizing genetic algorithms, validating data, and model evaluation.
Result: The experimental results show that the best model is naïve Bayes based on information gain and the genetic algorithm yields an accuracy rate of 86.34%.
Novelty: The main contribution to this research is proposing a new model of the best NB optimization model by applying an optimization algorithm in the search for feature selection to increase sentiment classification accuracy.
Keywords
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