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All Journal Creative Information Technology Journal Informasi Interaktif Jurnal Teknimedia: Teknologi Informasi dan Multimedia International Journal Artificial Intelligent and Informatics Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Ferry Wahyu Wibowo
Magister Teknik Informatika Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : Jurnal Teknimedia: Teknologi Informasi dan Multimedia

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ANALISIS SENTIMEN PENGGUNA APLIKASI BANK SYARIAH INDONESIA DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) Rachmawati Oktaria Mardiyanto; Kusrini Kusrini; Ferry Wahyu Wibowo
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.85

Abstract

Several new Islamic banking products have been created as a result of the increasing popularity of Islamic banking in Indonesia. Due to the technical advances made possible by the globalization era, all operations, including transactions, can be carried out easily and practically. One of the sharia banks that offers mobile banking services is Bank Syariah Indonesia. BSI Mobile still occupies the 49th position in the banking category according to statistics taken from Google Playstore, while other institutions are already in the top 20 positions. 5 linguists will annotate or manually label the app's 55,416 user-submitted reviews and ratings. 13568 review and rating data collected by app users after annotating or labeling and eliminating duplicate data will be used in this research. In the early stages of the sentiment analysis process, case folding, punctuation mark re-moval, stop word removal, and stemming were carried out on review and rating data. The Support Vector Machine (SVM) approach is used to evaluate training data and data testing using stemmed findings. In this study, the results of the training and precision tests were each worth 87.309%, and the results of the training and memory tests were both worth 86.958%. The training accuracy value is 85.87%, the projected sentiment analysis results have an accuracy rate of 85.87%, and the training results and precision testing are each worth 86.958%.
Co-Authors Arif Fajar Solikin Ema Utami Kusrini Kusrini Kusrini M. Ziaurrahman Rachmawati Oktaria Mardiyanto Rendy Bagus Pratama Yusuf Wahyu Setiya Putra