ANALISIS SENTIMEN ULASAN PENGGUNA TERHADAP APLIKASI LINKEDIN MENGGUNAKAN PENDEKATAN BERT

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dc.contributor.authorPutri, Rahmadea
dc.contributor.supervisorAminuddin, Al
dc.date.accessioned2023-09-29T03:53:01Z
dc.date.available2023-09-29T03:53:01Z
dc.date.issued2023-06
dc.description.abstractGoogle Play Store, one of the largest digital distribution platforms for downloading and uploading developed applications, was the focus of this research aimed at obtaining sentiment analysis results of LinkedIn application users. Sentiment analysis was used to evaluate the response of LinkedIn users and enable developers to improve the quality of LinkedIn applications in the future. User review data was collected through the scraping method and included 4,333 reviews, of which 2,296 were positive, 543 were neutral, and 1,494 were negative. Positive sentiments dominated the proportion of sentiments, at 53%. The model and method used for analysis were the pre-trained BERT model, known as IndoBERT, with average accuracies of 84% for validation data, respectively. An evaluation of the model through a confusion matrix yielded an accuracy of 80%, precision of 90%, recall of 84%, and F1-Score of 87%.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/123456789/11129
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectBERTen_US
dc.subjectConfusion Matrixen_US
dc.subjectIndoBERTen_US
dc.subjectLinkedInen_US
dc.subjectNLPen_US
dc.subjectSentimen Analysisen_US
dc.titleANALISIS SENTIMEN ULASAN PENGGUNA TERHADAP APLIKASI LINKEDIN MENGGUNAKAN PENDEKATAN BERTen_US
dc.typeArticleen_US

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