Library Support Vector Machine (LibSVM) Model for Coastal Assessment Sentiment Review
Abstract
Improving services as an effort to provide the convenience of tourist destinations, especially on the south coast of Java, is a demand placed on tourism managers, which in the long run will yield positive impacts. The assessment is conducted to determine whether the tourism destination give positive impressions to the tourists. The application of machine learning-based text mining technology, especially a sentiment review, is one of the solutions proposed to overcome this problem, therefore predictions of coastal tourism potential can be known beforehand. This research proposed a coastal sentiment review model using the library support vector machine (LibSVM) method. The process proposed a model optimization based on feature weights using the particle swarm optimization (PSO) algorithm as a model optimization to increase the accuracy level. Efforts to improve the accuracy of the proposed model are the main contribution of this research. The results of research and experiments on the proposed model produced the best model named LibSVM_IG+PSO using the information gain (IG). On the other hand, PSO-based LibSVM method generated an accuracy level of 88.97%. The model proposed in this research is expected to serve as a decision support for tourists, government, and tourism managers in assessing sentiment towards the coastal maritime tourism.
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