Rahmatulloh, Alam and Shofa, Rahmi Nur and Darmawan, Irfan and Ardiansah, Ardiansah Sentiment Analysis of Ojek Online User Satisfaction Based on the Naive Bayes and Net Brand Reputation Method. In: 2021 9th International Conference on Information and Communication Technology (ICoICT).
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19. Sentiment Analysis of Ojek Online User Satisfaction Based on the Naive Bayes and Net Brand Reputation Method.pdf Download (2MB) |
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2.B10-Korespondensi Sentiment Analysis.pdf Download (45kB) |
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2.B10-Similaritas Sentiment Analysis.pdf Download (1MB) |
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2.B10-Sertifikat Sentiment Analysis.pdf Download (265kB) |
Abstract
Gojek and Grab are the most popular online motorcycle taxis and are often used today in Indonesia, based on Hootsuite's survey. However, it is not yet known how the response from online motorcycle taxi users. So it is necessary to have a sentiment analysis of online motorcycle taxi users whether they are satisfied or dissatisfied with the drivers and Gojek and Grab companies' services. Twitter with 52% active users of all internet users in Indonesia allows users to write various topics so that to find out the level of user satisfaction with Gojek and Grab. Sentiment analysis can be used as a reference for the development of Gojek and Grab services in the future. They measure the level of satisfaction with the Net Brand Reputation (NBR) method from the Naïve Bayes classification results using the rapid miner tool. The rating with accuracy has an accuracy value of 99.80% for Gojek and 99.90% for Grab. This study shows that more tweets have negative opinions compared to positive opinions for Gojek and Grab. Namely 616 positive opinions and 2317 negative opinions for Gojek drivers, 3560 positive opinions and 6419 negative opinions for Gojek Company. 594 positive opinions, and 1866 negative opinions for Grab drivers. As well as 3516 positive opinions and 4407 negative opinions for Grab Companies. So the results of the sentiment analysis of online motorcycle taxi users are dissatisfaction with either the driver or the company.
| Item Type: | Conference or Workshop Item (Paper) |
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| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Teknik > Informatika > Artikel Dosen Informatika |
| Depositing User: | Mrs Linda Amelia Oktavia |
| Date Deposited: | 15 May 2023 05:49 |
| Last Modified: | 19 May 2023 09:02 |
| URI: | http://repositori.unsil.ac.id/id/eprint/9198 |
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