Comparing Classification Algorithm With Genetic Algorithm In Public Transport Analysis

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Congestion major cities in Indonesia caused by the proliferation of the use of private vehicles. Some people express their opinions and its opinion regarding public transport users through social media sites and other websites. This opinion can be used as a sentiment analysis material to find out whether the public transport service is positive or negative. The results of the sentiment analysis can help in the assessment and evaluation of the use of public transportation, it is also expected to improve services and facilities from public transportation so that the public tends to have a positive opinion. Based on the results of the sentiment analysis, it is expected that the community will switch to using public transportation which will certainly reduce congestion. In this study also added preprocessing stages by using the GataFramework framework to complete processes that cannot be done on RapidMiner tools. The method used in this study is sentiment analysis with the method of applying genetic algorithms for feature selection with comparative classification algorithms. Performed by testing the composition of various data. From the results of testing for the case in this study, it was found that the Support Vector Machine classification algorithm based on Genetic Algorithms had a fairly good average accuracy of 76.11% and AUC value of 0.778% with the Fair Classification diagnosis level compared to the three methods such as Naive Bayes, Support Vector Machine and Naive Bayes based on Genetic Algorithms. So that in this study Support Vector Machine classification algorithm based on Genetic Algorithm can be recommended as an algorithm classification good enough to analyze land transportation public sentiment. Based on the analysis it is expected that the public sentiment will switch to using public transport which would reduce congestion.