PRAYUDA, ADVERINO PUTRATAMA (2021) CLUSTERING AND IDENTIFY GAMES REVIEW ON STEAM STORE. Other thesis, Universitas Katholik Soegijapranata Semarang.
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Abstract
Modern entertainment can already be achieved very easily, one example is a game that can be purchased online and downloaded immediately. As a means, online stores provide a review feature as a means to convince buyers through feedback from other buyers. However, this feature is often misused for one-sided advantage, including the Steam platform which has a large number of users. This research will discuss whether Steam has fake reviews and, whether each review is a helpful review. These questions will be solved by using a data mining algorithm. The use of K-Means will be focused on determining the majority of reviews are Recommended or Not Recommended reviews. The first result is big upvotes on each review mostly come from 2 factors, which is Steam Curators and big developers. The second result is long review and total play hour is not a determinant for the number of helpful votes. The third result is the accuracy from clustering Review and Recommendation using K-Means algorithm show a high percentage, with 500 sample data, this algorithm can have 100% accuracy, but with different amount of sample data the accuracy score can changed, in example if I use 100 sample data, the algorithm can only achieve 84% accuracy.
| Item Type: | Thesis (Other) |
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| Subjects: | 000 Computer Science, Information and General Works > 004 Data processing & computer science |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | mr AM. Pudja Adjie Sudoso |
| Date Deposited: | 15 Oct 2021 03:12 |
| Last Modified: | 15 Oct 2021 03:12 |
| URI: | http://repository.unika.ac.id/id/eprint/27133 |
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