Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree
In today’s era of globalization, rapid technological advancements are driving innovation across various sectors, including the banking industry. One of the key digital innovations in banking is mobile banking (m-banking), which allows customers to perform transactions via smartphones. This study aim...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
Islamic University of Indragiri
2025-09-01
|
| Series: | Sistemasi: Jurnal Sistem Informasi |
| Subjects: | |
| Online Access: | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5472 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850044568344461312 |
|---|---|
| author | Muhammad Rizky Mawandhyka Akbar Irfan Pratama |
| author_facet | Muhammad Rizky Mawandhyka Akbar Irfan Pratama |
| author_sort | Muhammad Rizky Mawandhyka Akbar |
| collection | DOAJ |
| description | In today’s era of globalization, rapid technological advancements are driving innovation across various sectors, including the banking industry. One of the key digital innovations in banking is mobile banking (m-banking), which allows customers to perform transactions via smartphones. This study aims to analyze the sentiment of user reviews on the MyBCA application using three classification methods: Naive Bayes, Random Forest, and Decision Tree. A total of 5,000 user reviews were collected from the Google Play Store through web scraping techniques. The data was preprocessed using the TF-IDF weighting method and processed with Python programming language and the Scikit-Learn library. The dataset was split into 90% training data and 10% testing data. This study also applies the ISO 9126 standard for multi-label classification to assess software quality based on Usability, Efficiency, Functionality, Reliability, and Maintainability. Evaluation results indicate that Random Forest achieved the highest accuracy at 94.09%, outperforming Naive Bayes (81.77%) and Decision Tree (82.38%). This research contributes to the development of a sentiment-based evaluation method for mobile banking applications, integrating user feedback analysis with ISO 9126 quality standards, and offers a useful reference for improving digital banking services. |
| format | Article |
| id | doaj-art-147eea2ba65d4720afa4b922de154dfc |
| institution | DOAJ |
| issn | 2302-8149 2540-9719 |
| language | Indonesian |
| publishDate | 2025-09-01 |
| publisher | Islamic University of Indragiri |
| record_format | Article |
| series | Sistemasi: Jurnal Sistem Informasi |
| spelling | doaj-art-147eea2ba65d4720afa4b922de154dfc2025-08-20T02:54:54ZindIslamic University of IndragiriSistemasi: Jurnal Sistem Informasi2302-81492540-97192025-09-011452464247810.32520/stmsi.v14i5.54721200Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision TreeMuhammad Rizky Mawandhyka Akbar0Irfan Pratama1Universitas Mercu Buana YogyakartaUniversitas Mercu Buana YogyakartaIn today’s era of globalization, rapid technological advancements are driving innovation across various sectors, including the banking industry. One of the key digital innovations in banking is mobile banking (m-banking), which allows customers to perform transactions via smartphones. This study aims to analyze the sentiment of user reviews on the MyBCA application using three classification methods: Naive Bayes, Random Forest, and Decision Tree. A total of 5,000 user reviews were collected from the Google Play Store through web scraping techniques. The data was preprocessed using the TF-IDF weighting method and processed with Python programming language and the Scikit-Learn library. The dataset was split into 90% training data and 10% testing data. This study also applies the ISO 9126 standard for multi-label classification to assess software quality based on Usability, Efficiency, Functionality, Reliability, and Maintainability. Evaluation results indicate that Random Forest achieved the highest accuracy at 94.09%, outperforming Naive Bayes (81.77%) and Decision Tree (82.38%). This research contributes to the development of a sentiment-based evaluation method for mobile banking applications, integrating user feedback analysis with ISO 9126 quality standards, and offers a useful reference for improving digital banking services.https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5472sentiment analysis, mobile bankingmybcamachine learning ,iso-9126 |
| spellingShingle | Muhammad Rizky Mawandhyka Akbar Irfan Pratama Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree Sistemasi: Jurnal Sistem Informasi sentiment analysis, mobile banking mybca machine learning ,iso-9126 |
| title | Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree |
| title_full | Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree |
| title_fullStr | Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree |
| title_full_unstemmed | Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree |
| title_short | Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree |
| title_sort | sentiment analysis of mybca application user reviews using naive bayes random forest and decision tree |
| topic | sentiment analysis, mobile banking mybca machine learning ,iso-9126 |
| url | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5472 |
| work_keys_str_mv | AT muhammadrizkymawandhykaakbar sentimentanalysisofmybcaapplicationuserreviewsusingnaivebayesrandomforestanddecisiontree AT irfanpratama sentimentanalysisofmybcaapplicationuserreviewsusingnaivebayesrandomforestanddecisiontree |