Analysis of MyIndiHome Application User Sentiment Using the Support Vector Machine Method

Authors

  • Muhyi Universitas Bina Sarana Informatika
  • Hylenarti Hrtyana Universitas Bina Sarana Informatika
  • Hernawati Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.58777/ise.v3i2.490

Keywords:

Sentiment Analysis, MyIndiHome, Support Vector Machine (SVM), User Perception, Digital Applications

Abstract

The development of information technology in the era of the Industrial Revolution 4.0 has encouraged service providers to continue to improve the quality of their digital services, including PT. Telkom Access through the MyIndiHome application. This application makes it easier for customers to access various services, but there are still complaints regarding features and ease of use. This study aims to analyze the sentiment of MyIndiHome application users using the Support Vector Machine (SVM) method to evaluate customer perception and satisfaction levels. The data collection method was carried out through Simple Classification Results (based on 70 respondents): Interviews with nine purposively selected informants, Distribution of questionnaires to 61 interview respondents of MyIndiHome application users, which were then analyzed using pre-processing and classification techniques to distinguish sentiment into positive and negative. The analysis results show that most users have a positive perception of the application, especially in terms of ease of reporting interruptions and the completeness of features. However, there are still obstacles to ease of use, especially for users less familiar with technology. The SVM method has proven effective in classifying user sentiment even with limited data. This study recommends improving the user interface and education features to optimize the overall use of the MyIndiHome application.

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Published

2025-10-08

How to Cite

Muhyi, M., Hylenarti Hrtyana, H. H., & Hernawati, H. (2025). Analysis of MyIndiHome Application User Sentiment Using the Support Vector Machine Method. Informatics and Software Engineering, 3(2), 41–47. https://doi.org/10.58777/ise.v3i2.490

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