Sentiment review of coastal assessment using neural network and naïve Bayes

Oman Somantri, Santi Purwaningrum, Ratih Hafsarah Maharrani


An assessment of a place will provide an overview for other people whether the place is feasible to be visited or not. Assessment of coastal places will provide a separate assessment for potential visitors in considering visitation. This article proposes a model using the neural network (NN) and naïve Bayes (NB) methods to classify sentiment toward coastal assessments. The proposed NN and NB models are optimized using information gain (IG) and feature weights, namely particle swarm optimization (PSO) and genetic algorithm (GA) which are carried out to increase the level of classification accuracy. Based on the experimental results, the best level of accuracy for the classification of coastal assessments is 87.11% and is named the NB IG+PSO model. The best model obtained is a model that can be used as a decision support for potential beach visitors in deciding to visit the place.


Coastal assessment; Feature weight; Naïve Bayes; Neural network; Sentiment review;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).