Analisis Kepuasan Pengguna Aplikasi Gopay Menggunakan Algoritma Naïve Bayes Dan K-Fold Cross Validation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AEEEY7T86" target="_blank" >RIV/00216208:11320/26:EEEY7T86 - isvavai.cz</a>
Result on the web
<a href="https://repository.bsi.ac.id/repo/71147/ANALISIS-KEPUASAN-PENGGUNA-APLIKASI-GOPAY-MENGGUNAKAN-ALGORITMA-NAÏVE-BAYES-DAN-K-FOLD-CROSS-VALIDATION" target="_blank" >https://repository.bsi.ac.id/repo/71147/ANALISIS-KEPUASAN-PENGGUNA-APLIKASI-GOPAY-MENGGUNAKAN-ALGORITMA-NAÏVE-BAYES-DAN-K-FOLD-CROSS-VALIDATION</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Analisis Kepuasan Pengguna Aplikasi Gopay Menggunakan Algoritma Naïve Bayes Dan K-Fold Cross Validation
Original language description
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 93,22%, with a precision of 92,60%, recall of 95,58%, and an f1-score of 94.11%. Validation using 10-fold cross-validation resulted in an average accuracy of 92.38%. 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.
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Jurnal Ilmiah Informatika
ISSN
2615-1049
e-ISSN
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Volume of the periodical
2025
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
60
Pages from-to
1-60
UT code for WoS article
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EID of the result in the Scopus database
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