Implementasi Support Vector Machine dalam Analisis Sentimen Ulasan Aplikasi IndiHome TV di Google Play Store

Authors

  • Agus Fajar Riany Sistem Informasi, UIN Raden Fatah Palembang
  • Fenny Purwani Sistem Informasi, UIN Raden Fatah Palembang
  • Irfan Dwi Jaya Sistem Informasi, UIN Raden Fatah Palembang

DOI:

https://doi.org/10.33020/saintekom.v15i2.954

Keywords:

sentiment analysis, text mining, Support Vector Machine, indihome tv

Abstract

Sentiment analysis is used to determine the responses or opinions of a group or individual regarding a topic of discussion in the context of the entire document. The Indihome TV application is currently widely used by the public, so that reviews of the Indihome TV application on the Google Play Store are very numerous. The exact number of reviews given by users is not yet known based on their sentiment class. Therefore, a method is needed to facilitate the analysis of these user reviews. The purpose of this study is to determine the polarity of sentiment towards the Indihome TV application and to determine the performance and accuracy resulting from the application of the Support Vector Machine algorithm. The method used to convert unstructured reviews into structured reviews uses the Text Mining method. The results of this study indicate that using the SVM algorithm in sentiment analysis of the Indihome TV application data produces the highest accuracy value at a ratio of 90:10 at 94%. Furthermore, from the results of data visualization, the most frequently appearing words are applications, watch, channel, open, please, good, login, indihome, complete and so on.

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Published

30-09-2025

How to Cite

Riany, Agus Fajar, Fenny Purwani, and Irfan Dwi Jaya. 2025. “Implementasi Support Vector Machine Dalam Analisis Sentimen Ulasan Aplikasi IndiHome TV Di Google Play Store”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 15 (2):177-93. https://doi.org/10.33020/saintekom.v15i2.954.

Issue

Vol. 15 No. 2 (2025): September 2025

Section

Articles

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Copyright (c) 2025 Agus Fajar Riany, Fenny Purwani, Irfan Dwi Jaya

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