AKBAR, MOHAMMAD SATRIO (2024) ANALISIS SENTIMEN TERHADAP REVIEW APLIKASI GRAB DI GOOGLE PLAYSTORE BERDASARKAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE. S1 thesis, Universitas Mercu Buana - Menteng.
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Abstract
Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan pengguna aplikasi Grab di Google Play Store menggunakan metode Naïve Bayes dan Support Vector Machine. Penelitian ini menggunakan data dalam bahasa Indonesia dan menganalisis sentimen dalam tiga kelas: positif, dan negatif. Metode Naïve Bayes dan Support Vector Machine digunakan untuk mengklasifikasikan ulasan pengguna ke dalam kategori sentimen yang sesuai. Penelitian ini menggunakan API Google Play Store dan library Google_play_scrapper untuk mengumpulkan data ulasan pengguna. Data yang berhasil dikumpulkan sebanyak 1195 ulasan. Hasil analisis sentimen diharapkan dapat memberikan wawasan berharga bagi Grab dalam meningkatkan pengalaman pengguna dan kualitas layanan aplikasi mereka. This research aims to conduct sentiment analysis of Grab app user reviews on Google Play Store using Naïve Bayes and Support Vector Machine methods. This research uses data in Indonesian and analyzes sentiment in three classes: positive, and negative. Naïve Bayes and Support Vector Machine methods are used to classify user reviews into the appropriate sentiment categories. This research uses Google Play Store API and Google_play_scrapper library to collect user review data. The data collected was 1195 reviews. The sentiment analysis results are expected to provide valuable insights for Grab in improving user experience and service quality of their app.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41520010218 |
| Uncontrolled Keywords: | Naïve Bayes , Python , Analisis Sentimen, Support Vector Machine Naïve Bayes , Sentiment Analysis , Python , Support Vector Machine |
| Subjects: | 600 Technology/Teknologi > 600. Technology/Teknologi |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | SILMI KAFFA MARISKA |
| Date Deposited: | 25 Jun 2024 02:49 |
| Last Modified: | 25 Jun 2024 02:49 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/89150 |
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