Fahry Amzar, . (2024) ANALISIS SENTIMEN ULASAN APLIKASI SEABANK PADA SITUS GOOGLE PLAY STORE MENGGUNAKAN NAÏVE BAYES CLASSIFIER. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
In an era of rapid advances in information and communication technology, the SeaBank application has attracted significant attention due to its attractive high interest rates on savings and deposits, ranging from 5% to 6%. This makes digital banking more attractive than conventional banks. This research utilized the Naïve Bayes Classifier method to analyze sentiment from 820,000 reviews with the data sample used being 2492 user reviews, which were categorized into positive and negative, excluding neutral reviews. The analysis showed that the majority of reviews, around 54.7%, were negative. Words that frequently appear in positive reviews include “good,” “easy,” and “fast,” while negative reviews often highlight the words of “disappointed,” “difficult,” and “failed.” Evaluation of the classification model shows that the Naïve Bayes Classifier has an accuracy of 93.81%, with an F1-score of 93.8%, recall of 93.81%, and precision of 93.82%. The conclusions of this study confirm the effectiveness of the Naïve Bayes Classifier in analyzing the sentiment of banking app reviews, providing valuable insights for future SeaBank app development.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2010512021] [Pembimbing 1: Widya Cholil] [Pembimbing 2: Intan Hesti Indriana] [Penguji 1: Theresia Wati] [Penguji 2: Rio Wirawan] |
| Uncontrolled Keywords: | Sentiment Analysis, Naïve Bayes Classifier, SeaBank. |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Sistem Informasi (S1) |
| Depositing User: | FAHRY AMZAR |
| Date Deposited: | 09 Sep 2024 07:54 |
| Last Modified: | 09 Sep 2024 07:54 |
| URI: | http://repository.upnvj.ac.id/id/eprint/30188 |
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