ANALISIS KEPUASAN PENGGUNA APLIKASI GOPAY MENGGUNAKAN ALGORITMA NAÏVE BAYES  DAN K-FOLD CROSS VALIDATION

Asmaul Usnah; Fuad Nur  Hasan; Antonius Yadi Kuntoro

doi:10.33884/jif.v13i02.10276

Authors

Asmaul Usnah Universitas Bina Sarana Informatika
Fuad Nur Hasan Universitas Bina Sarana Informatika
Antonius Yadi Kuntoro Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.33884/jif.v13i02.10276

Keywords:

K-Fold Cross Validation, User Satisfaction, Naïve Bayes, Text Mining

Abstract

The rapid advancement of digital technology has significantly increased the adoption of digital wallet services in Indonesia, one of which is the GoPay application. This study aims to analyze user satisfaction with GoPay based on user reviews from the Google Play Store. The classification method used is the Naïve Bayes algorithm, with model validation performed using the K-Fold Cross Validation technique. A total of 3,000 reviews were collected through web scraping and then preprocessed using several text preprocessing steps including cleansing, case folding, tokenizing, stopword removal, and stemming. The data was automatically labeled using the IndoBERT model and classified into two satisfaction categories. The classification results show that the Naïve Bayes algorithm achieved an accuracy of 92.46%, with a precision of 92.25%, recall of 94.70%, and an f1-score of 93.46%. Validation using 10-fold cross-validation resulted in an average accuracy of 92.23%. These results indicate that the model demonstrates strong classification performance and stable generalization on unseen data. This research is expected to contribute to improving GoPay's service quality and serve as a reference for the implementation of machine learning techniques in user satisfaction analysis.

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