Analisis Sentimen Ulasan Pengguna Aplikasi Penjualan Pulsa Menggunakan Algoritma Naïve Bayes Classifier

Authors

  • Azis Syafi'i Universitas Islam Negeri Sultan Syarif Kasim Riau
  • M. Afdal Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Eki Saputra Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau

DOI:

https://doi.org/10.32493/jtsi.v7i3.41364

Keywords:

Sentiment Analysis; Pulse Sales Application; DigiPOS; Naïve Bayes Classifier (NBC); Tetra Pulsa; Orderkuota

Abstract

Many credit sales applications are commonly used by outlets or counters, such as DigiPOS, Tetra Pulsa, and Orderkuota. However, there are common problems with these applications such as prices that are starting to be less competitive, difficult to use, transactions that often fail, security, service and others. Therefore, this study analyzes the sentiment of user reviews to identify the strengths and weaknesses of these apps, to help developers improve their services, and to guide agents in choosing the right app. NBC algorithm is proposed to be used for sentiment classification. The analysis results show the dominance of positive sentiments on all apps, with Tetra Pulsa having the highest positive sentiment (97.10%), followed by Orderkuota (84.40%) and DigiPOS (64.00%). Then the results of the implementation of the NBC algorithm can perform sentiment classification well. Tetra Pulsa application has an accuracy of 97.10%, Orderkuota 92.39%, and DigiPOS 91.10%. The results of this study can be considered to evaluate and improve the application so that it can provide better service to users of the credit sales application.

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Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 Juli 2024

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Published

2024-07-31

How to Cite

Syafi’i, A., Afdal, M., Saputra, E., & Novita, R. (2024). Analisis Sentimen Ulasan Pengguna Aplikasi Penjualan Pulsa Menggunakan Algoritma Naïve Bayes Classifier. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(3), 1300–1308. https://doi.org/10.32493/jtsi.v7i3.41364

Issue

Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

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Copyright (c) 2024 Azis Syafi'i, M. Afdal, Eki Saputra, Rice Novita

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