BibTex Citation Data :
@article{J.Gauss35667, author = {Jessica Rieuwpassa and Sugito Sugito and Tatik Widiharih}, title = {IMPLEMENTASI METODE NAIVE BAYES CLASSIFIER UNTUK KLASIFIKASI SENTIMEN ULASAN PENGGUNA APLIKASI NETFLIX PADA GOOGLE PLAY}, journal = {Jurnal Gaussian}, volume = {12}, number = {3}, year = {2024}, keywords = {Sentiment Analysis; Naive Bayes Classifier; Streaming Service Application; Netflix}, abstract = { The COVID-19 pandemic has led to restrictions on activities in public places or facilities, such as cinemas. This has resulted in increased users of streaming service applications such as Netflix where users can access videos or movies online. Netflix users continue to increase from year to year, but its users began to decrease along with other streaming applications. Related to this, sentiment analysis was carried out on the classification of positive and negative reviews given by users on the Google Play website. The classification is expected to produce good accuracy and be analyzed so that it can be useful information for Netflix and potential users of streaming applications. The Naive Bayes Classifier method is a classification algorithm that is easy to apply and has high effectiveness for classifying text. This method utilizes the concept of conditional probability and has a strong assumption of independence. This study uses 2.850 Netflix application review data on Google Play which is then processed and divided into training data and test data with a ratio of 80:20. Classification with the Naive Bayes Classifier produces an accuracy value of 87,33%, a precision value of 87,6%, a recall value of 93,53%, and an F-measure value of 90,47% so it can be concluded that the performance of the Naive Bayes method is good for classifying user reviews of the Netflix. }, issn = {2339-2541}, pages = {362--371} doi = {10.14710/j.gauss.12.3.362-371}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/35667} }
Refworks Citation Data :
The COVID-19 pandemic has led to restrictions on activities in public places or facilities, such as cinemas. This has resulted in increased users of streaming service applications such as Netflix where users can access videos or movies online. Netflix users continue to increase from year to year, but its users began to decrease along with other streaming applications. Related to this, sentiment analysis was carried out on the classification of positive and negative reviews given by users on the Google Play website. The classification is expected to produce good accuracy and be analyzed so that it can be useful information for Netflix and potential users of streaming applications. The Naive Bayes Classifier method is a classification algorithm that is easy to apply and has high effectiveness for classifying text. This method utilizes the concept of conditional probability and has a strong assumption of independence. This study uses 2.850 Netflix application review data on Google Play which is then processed and divided into training data and test data with a ratio of 80:20. Classification with the Naive Bayes Classifier produces an accuracy value of 87,33%, a precision value of 87,6%, a recall value of 93,53%, and an F-measure value of 90,47% so it can be concluded that the performance of the Naive Bayes method is good for classifying user reviews of the Netflix.
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