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
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