Analisis Sentimen Pengguna Terhadap Aplikasi Netflix di Google Play Store Menggunakan Support Vector Machine

Simple item page
dc.contributor.authorAzura, Endah
dc.contributor.supervisorAminuddin, Al
dc.date.accessioned2023-06-07T02:31:35Z
dc.date.available2023-06-07T02:31:35Z
dc.date.issued2023-03
dc.description.abstractDuring the COVID-19 pandemic, one of the activities commonly carried out by the community, such as going out of the house, was limited by the PSBB (Large-Scale Social Restrictions). This made people stay at home more and use internet access, one of which was by streaming movies using the Netflix application. Users can provide reviews of application services in the review feature on the Google Play Store. The existence of this feature is one of the considerations for prospective users to download the application. Therefore it is necessary to do sentiment analysis to find out how the user's sentiment is on the application. Therefore, this study aimed to analyze reviews and discovered the results of the accuracy of public sentiment reviews on a movie streaming application, namely Netflix on the Google Play Store using the Support Vector Machine method. In this study, data collection was carried out using web scraping techniques on Google Colaboratory. The data used was data when the pandemic spread to Indonesia, namely in 2020. The method used in this study resulted in an average accuracy of 85%.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alamen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.otherElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/11004
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectSentiment analysisen_US
dc.subjectNetflix applicationen_US
dc.subjectSupport Vector Machineen_US
dc.titleAnalisis Sentimen Pengguna Terhadap Aplikasi Netflix di Google Play Store Menggunakan Support Vector Machineen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Endah Azura_compressed.pdf
Size:
201.65 KB
Format:
Unknown data format
Description:
artikel
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Sistem Infomasi