E3S Web Conf.
Volume 202, 2020The 5th International Conference on Energy, Environmental and Information System (ICENIS 2020)
|Number of page(s)||12|
|Section||Smart Information System|
|Published online||10 November 2020|
Implementation of Integrated Bayes Formula and Support Vector Machine for Analysing Airline’s Passengers Review
1 Master Program of Information System, School of Postgraduate Studies, Diponegoro University, Indonesia
2 Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia
3 Department of Informatics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia
* Corresponding author: firstname.lastname@example.org
Nowadays, the utilization of Internet of Things (IoT) is commonly used in the tourism industry, including aviation, where passengers of flight services can rate their satisfaction levels towards the product and service they use by writing their reviews in the form of text-based data on many popular websites. These passenger reviews are collections of potential big data and can be analyzed in order to extract meaningful informations. Some text mining algorithms are already in common use, including the Bayes formula and Support Vector Machine methods. This research proposes an implementation of the Bayes and SVM methods where these algorithms will operate independently yet integrated with other modules such as input data, text pre-processing and shows output result concisely in one single information system. The proposed system was successfully delivered 1000 documents of passenger reviews as input data, then after implemented the pre-processing method, the Bayes formula was used to classify the document reviews into 5 categories, including plane condition, flight comfort, staff service, food and entertainment, and price. While simultanously, the positive and negative sentiment contained in the review document was analyzed with SVM method and shows the accuracy score of 83.6% for a training to testing set ratio of 50:50, while 82.75% accuracy for the 60:40 ratio, and 83.3% accuracy for the 70:30 ratio. This research shows that two different text mining algorithms can be implemented simultaneously in a effective and efficient way, while still providing an accurate and satisfying performance results in one integrated information system.
Key words: text mining / Bayes formula / Support Vector Machine / integrated system / sentiment analysis
© The Authors, published by EDP Sciences, 2020
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