Prabowo, Galih Arum (2023) ASPECT-BASED SENTIMENT ANALYSIS IPHONE 14 PRO MENGGUNAKAN ALGORITMA XGBOOST DAN METODE PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
YouTube is one of the applications that many Indonesians will visit in 2022. This is used by many parties as a forum for branding and disseminating information, one channel that is quite attractive to YouTube users, especially gadget fans, is GadgetIn. This is useful for prospective users in searching for gadgets to buy. Apart from the video reviews provided, prospective users can also view information reviews provided by other YouTube users to find out other users' experiences in using the product. This review is also useful for application developers to be able to find out what aspects are good and what aspects are lacking so that they can be used as evaluation material for the future. This research will implement the XGBoost algorithm and the PSO optimization method to carry out sentiment analysis based on aspects in the comments column of the Indonesian iPhone 14 Pro review YouTube video on the GadgetIn channel, which aims to measure the level of accuracy of the XGBoost algorithm in classifying reviews into two classes, namely the positive sentiment class. -negative, as well as knowing the strengths and weaknesses of the iPhone iPhone 14 pro. The dataset used is 1000 pieces of data scraped from the YouTube comments column. From the test results, a fairly high level of accuracy was obtained, namely between 89% and 97%. Apart from that, from this research it can also be seen that the advantages are in the brand image aspect. As for its weaknesses, the iPhone 14 Pro still gets a lot of negative sentiment, especially regarding the design aspect
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76.6 Computer Programming T Technology > T Technology (General) |
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| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Galih Arum | ||||||||||||
| Date Deposited: | 23 Nov 2023 04:13 | ||||||||||||
| Last Modified: | 23 Nov 2023 04:13 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/18791 |
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