ANALISIS SENTIMEN TERHADAP DATA ULASAN PENGGUNA APLIKASI FLIP.ID PADA GOOGLE PLAY DENGAN METODE SVM

Taqillah, Muhammad Elraz (2024) ANALISIS SENTIMEN TERHADAP DATA ULASAN PENGGUNA APLIKASI FLIP.ID PADA GOOGLE PLAY DENGAN METODE SVM. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The largest banks in Indonesia charge an administration fee of IDR 6,500.00 for inter-bank transfers, which is often rejected. The presence of fintech applications such as OVO, DANA and OY! Indonesia, which offer transfers without administration fees, has increased people's interest in making the switch. One of the most popular fintech applications is Flip.id, with more than 10 million downloads and a rating of 4.4 in the Google Play Store. Users often share their opinions and perspectives through comments in Flip.id application reviews. User opinions on these reviews vary and are sometimes idiosyncratic or exaggerated. Sentiment analysis is used to evaluate user opinions and reviews. The research data comes from user reviews on the Google Play Store for Flip.id, which are then analysed using the Support Vector Machine (SVM) method. The test results show that the SVM method is effective in classifying the sentiment of reviews of the Flip.id application, with an accuracy of 95%. These results show that sentiment analysis with SVM can provide valuable insights into user perceptions of applications, which in turn can be used for further service improvement and development. Keywords: Sentiment Analysis, Fintech, Flip.id, Classification, Support Vector Machine Bank-bank terbesar di Indonesia menetapkan biaya administrasi sebesar Rp6.500,00 untuk transfer antar bank, yang seringkali membuat masyarakat merasa keberatan. Kehadiran aplikasi fintech seperti OVO, DANA, dan OY! Indonesia yang menawarkan transfer tanpa biaya administrasi telah menarik minat masyarakat untuk beralih. Salah satu aplikasi fintech yang sangat diminati adalah Flip.id, dengan lebih dari 10 juta unduhan dan rating 4.4 di Google Play Store. Pengguna sering memberikan opini dan perspektif mereka melalui komentar di ulasan aplikasi Flip.id. Opini pengguna pada ulasan ini berfluktuasi dan kadang-kadang unik atau berlebihan. Untuk mengevaluasi opini dan ulasan pengguna, digunakan analisis sentimen. Data penelitian berasal dari ulasan pengguna di Google Play Store untuk Flip.id, yang kemudian dianalisis menggunakan metode Support Vector Machine (SVM). Hasil pengujian menunjukkan bahwa metode SVM efektif dalam melakukan klasifikasi sentimen terhadap ulasan aplikasi Flip.id, dengan akurasi sebesar 95%. Hasil ini menunjukkan bahwa analisis sentimen dengan SVM dapat memberikan wawasan berharga tentang persepsi pengguna terhadap aplikasi, yang pada gilirannya dapat digunakan untuk perbaikan dan pengembangan layanan lebih lanjut. Kata kunci: Analisis Sentimen, Fintech, Flip.id, Klasifikasi, Support Vector Machine

Item Type: Thesis (S1)
Call Number CD: FIK/SI. 24 104
NIM/NIDN Creators: 41820010110
Uncontrolled Keywords: Analisis Sentimen, Fintech, Flip.id, Klasifikasi, Support Vector Machine
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
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 020 Library and Information Sciences/Perpustakaan dan Ilmu Informasi > 025 Operations, Archives, Information Centers/Operasional Perpustakaan, Arsip dan Pusat Informasi, Pelayanan dan Pengelolaan Perpustakaan > 025.4 Subject Analysis and Control/Subjek Analisis dan Kontrol Perpustakaan > 025.46 Classification of Specific Subject/Klasifikasi 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
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: khalimah
Date Deposited: 27 Jul 2024 02:26
Last Modified: 27 Jul 2024 02:26
URI: http://repository.mercubuana.ac.id/id/eprint/89853

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