PERBANDINGAN ALGORITMA INDOBERT, NAIVE BAYES, SVM, DAN KNN TERHADAP ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI M-PASPOR DI GOOGLE PLAY STORE

PRAKOSO, SINDU and Tania, Ken Ditha (2024) PERBANDINGAN ALGORITMA INDOBERT, NAIVE BAYES, SVM, DAN KNN TERHADAP ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI M-PASPOR DI GOOGLE PLAY STORE. Undergraduate thesis, Universitas Sriwijaya.

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Abstract

The development of information technology is very fast along with the progress of the times making all aspects of life switch to digital. The M-Passport application is an application that can be used by the public to apply for new passports and passport replacements online. The M-Passport application has various responses, both positive, negative and neutral, from users listed in reviews on the Google Play Store. With the large amount of review data, sentiment analysis is needed to understand sentiment patterns efficiently. This study compares the performance of the Naïve Bayes, SVM, KNN, and IndoBERT algorithms in sentiment analysis of M-Passport application reviews.. The results show that the IndoBERT algorithm excels with 99% accuracy, followed by SVM with 98% accuracy, while Naïve Bayes and KNN have 91% and 67% accuracy respectively.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sentiment Analysis, M-Paspor, IndoBERT,Naïve Bayes, SVM, KNN
Subjects:	T Technology > T Technology (General) > T58.6-58.62 Management information systems
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Sindu Prakoso
Date Deposited:	10 Jan 2025 05:01
Last Modified:	10 Jan 2025 05:01
URI:	http://repository.unsri.ac.id/id/eprint/163667

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