Komparasi Metode Svm, K-Nn Dan NBC Pada Analisis Sentimen

Abstract

The beauty of Bali raises many comments about how a trip to Indonesia is not complete without going to Bali. In the tourism industry the application of tourist satisfaction and perspective is very important, but most of them still apply surveys. The survey-based approach has weaknesses such as operational costs, the potential for data duplication, and a lack of comprehensiveness. Sentiment analysis of natural tourism objects is performed by classifying positive and negative comments in the Jatiluwih tourist comment dataset. The focus of this panel's sentiment analysis is on comments related to the Natural Tourism Attractiveness Criteria. According to the Directorate General of Forest Protection and Nature Conservation in 2003, the criteria for natural tourism objects are tourist attraction, market potential, accessibility, socio-economic environmental conditions, public services, climate conditions, supporting facilities and infrastructure, and the availability and safety of clean water. This study compares the SVM, K-NN, and NBC methods. This study aims to provide a comprehensive analysis of the performance of each method using the confusion matrix. The results showed that the K-NN method was superior to SVM and NBC in terms of testing accuracy and precision, where accuracy on K-NN gave a value of 93.4%, SVM 93.1%, and NBC 87.9%.

Sentiment Analysis of Madura Tourism Opinion Using Support Vector Machine (SVM)

“…The results show that the K-NN method has higher accuracy and testing precision than SVM and NBC. The K-NN method provides a value of 93.4%, SVM 93.1%, and NBC 87.9% [14].…”