Komparasi Algoritma Text Mining Untuk Klasifikasi Review Hotel

Andi Taufik


The use of information technology using the internet is very easy to find information, so its users do not have to go to the place to find information, website and mobile applications internet users can directly get the information you want. Every Manager world tourism now provides the details about the tourism products offered. Very useful information at this time because people tend to look for quick information in the booking through the review of others in social media, blogs and websites. The importance of the review of the hotel as a source of information for travelers will plan trips. Currently known methods of classification of the highest accuracy in classifying hotels Indonesia-language review. So I need to know how Naïve Bayes algorithms of accuracy, Support Vector Machine, Decission Tree (C4.5) and Naïve Bayes Method with Particle Swarm Optimization Feature Selection. The results obtained from the comparison of four methods of such algorithms, a better level of accuracy in the classification review of hotel indonesian using an algorithm Decission Tree (C4.5) 96.94% While achieving the fit method of optimization of the Nave bayes by using Particle Swarm Optimization feature Selection of 95.91%, accuracy using Naive Bayes Algorithm of 89.98 and the accuracy of the model of Support Vector Machine of 89.86.


Keywords: Reviews Hotel, Naive Bayes, PSO, SVM, Decission Tree, C4.5

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DOI: https://doi.org/10.31294/jtk.v4i2.3461


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