Analisis Sentimen Aplikasi BYOND by BSI di Google Play Store Menggunakan Metode SVM

The BYOND by BSI application has received various user reviews on the Google Play Store, reflecting user perceptions and satisfaction. Sentiment analysis is needed to understand these opinion patterns and support service quality improvement. This study aims to analyze the sentiment of BYOND by BSI user reviews by applying the Support Vector Machine (SVM) method. Review data were collected from the Google Play Store and processed through text preprocessing stages followed by SVM classification modeling. The results show a classification accuracy of 87%, with strong performance in the Positive class (F1-score 0.91) and Negative class (F1-score 0.88), but SVM failed to detect the Neutral class due to data imbalance, where the Neutral class accounted for only 5.85% of the total samples. In conclusion, these findings highlight the importance of handling class imbalance through approaches such as resampling, ensemble algorithms, or class-weight optimization in SVM to improve the accuracy of Neutral sentiment detection.

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Publication Details

Journal
Jurnal Accounting Information System (AIMS)
Published
2025-09-30
DOI
https://doi.org/10.32627/aims.v8i2.1583
Primary Topic
Edcuational Technology Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Analisis Sentimen Aplikasi BYOND by BSI di Google Play Store Menggunakan Metode SVM

Elia Setiana, Imannudin Akbar, Arnold Ropen Sinaga, Acep Hendra et al.
Jurnal Accounting Information System (AIMS)
Edcuational Technology Systems
article

Analisis Sentimen Aplikasi BYOND by BSI di Google Play Store Menggunakan Metode SVM

Elia Setiana, Imannudin Akbar, Arnold Ropen Sinaga, Acep Hendra, Titan Parama Yoga
article en

Abstract

The BYOND by BSI application has received various user reviews on the Google Play Store, reflecting user perceptions and satisfaction. Sentiment analysis is needed to understand these opinion patterns and support service quality improvement. This study aims to analyze the sentiment of BYOND by BSI user reviews by applying the Support Vector Machine (SVM) method. Review data were collected from the Google Play Store and processed through text preprocessing stages followed by SVM classification modeling. The results show a classification accuracy of 87%, with strong performance in the Positive class (F1-score 0.91) and Negative class (F1-score 0.88), but SVM failed to detect the Neutral class due to data imbalance, where the Neutral class accounted for only 5.85% of the total samples. In conclusion, these findings highlight the importance of handling class imbalance through approaches such as resampling, ensemble algorithms, or class-weight optimization in SVM to improve the accuracy of Neutral sentiment detection.

Jurnal Accounting Information System (AIMS)Vol. 8(2)
Universitas Informatika dan Bisnis Indonesia (ID)
Openalex Percentile: Top 85%
Edcuational Technology Systems
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