ANALISIS SENTIMEN PADA ULASAN APLIKASI SEABANK DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAIVE BAYES

PERMATASARI, NADHIIFAH (2024) ANALISIS SENTIMEN PADA ULASAN APLIKASI SEABANK DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAIVE BAYES. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This research is conducted in response to the dynamic developments in the banking sector in Indonesia, particularly driven by the COVID-19 pandemic, where the community is shifting towards online transactions. The primary focus of this study is SeaBank, a digital bank that has recently been widely used due to its ease of registration and the facilities and benefits it offers. Involving more than 5 million users and achieving an average rating of 4.8 on the Google Play Store, SeaBank receives diverse reviews but tends to lean towards positive reception. The research applies a quantitative experimental approach and utilizes web scraping techniques to collect a dataset comprising 4,000 reviews out of a total of 460,000 user reviews. The Naïve Bayes model, trained with implemented techniques to enhance accuracy, demonstrates a high accuracy result of 92.34%. Overall, this study proves that the Naïve Bayes model is reliable for analyzing sentiment in SeaBank application reviews. Additionally, the research identifies potential improvements that can be implemented to enhance the model's performance in the future. Keywords: Naïve Bayes Algorithm, SeaBank, Google Play Store, Digital Bank Penelitian ini dilakukan seiring dengan perkembangan dinamika perbankan di Indonesia khususnya dipacu oleh pandemi COVID-19, di mana masyarakat beralih ke transaksi online. Fokus utama penelitian ini adalah SeaBank, yaitu bank digital yang belakangan ini digunakan oleh banyak orang karena kemudahan dalam pendaftaran serta fasilitas dan keuntungan yang diberikan. Dengan melibatkan lebih dari 5 juta pengguna dan meraih penilaian rata-rata 4.8 di Google Play Store, SeaBank banyak menerima ulasan beragam namun lebih condong ke penerimaan positif. Metode penelitian yang diterapkan menggunakan pendekatan kuantitatif eksperimental dan memanfaatkan teknik web scraping untuk mengumpulkan dataset sejumlah 4.000 ulasan dari 460.000 ulasan pengguna keseluruhan. Model Naïve Bayes yang dilatih dengan penerapan teknik yang dibangun untuk meningkatkan akurasi, menunjukkan hasil akurasi yang tinggi mencapai 92,34%. Secara keseluruhan, penelitian ini membuktikan bahwa model Naïve Bayes dapat diandalkan untuk menganalisis sentimen ulasan aplikasi SeaBank. Selain itu, penelitian ini juga mengidentifikasi potensi perbaikan yang dapat diimplementasikan untuk meningkatkan kinerja model di masa depan. Kata Kunci : Algoritma Naïve Bayes, SeaBank, Google Play Store, Bank Digital

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 24 066
Call Number: SIK/15/24/060
NIM/NIDN Creators: 41519120020
Uncontrolled Keywords: Algoritma Naïve Bayes, SeaBank, Google Play Store, Bank Digital
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data > 005.5 General Purpose Application Programs/Program Aplikasi dengan Kegunaan Khusus
100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 154 Subconscious and Altered States and Process/Psikologi Bawah Sadar > 154.6 Sleep Phenomena/Fenomena Tidur > 154.63 Dreams/Mimpi > 154.634 Analysis/Analisis
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 651 Office Services/Layanan Kantor > 651.8 Computer Application for Office Management/Aplikasi Komputer untuk Manajemen Perkantoran
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: khalimah
Date Deposited: 14 Mar 2024 09:10
Last Modified: 20 Mar 2024 02:31
URI: http://repository.mercubuana.ac.id/id/eprint/87141

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