Sentiment Analysis of National Health Security Mobile Application Review Using Machine Learning
It is widely acknowledged that digital transformation provides the opportunity for business process improvements. We can see many businesses, specifically in Indonesia's private and public sectors, leveraging technology to provide better service to their stakeholders. This research seeks insigh...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
BPJS Kesehatan
2024-12-01
|
| Series: | Jurnal Jaminan Kesehatan Nasional |
| Subjects: | |
| Online Access: | https://jurnal-jkn.bpjs-kesehatan.go.id/index.php/jjkn/article/view/269 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850058001140940800 |
|---|---|
| author | Deta Novian Anantika Putra |
| author_facet | Deta Novian Anantika Putra |
| author_sort | Deta Novian Anantika Putra |
| collection | DOAJ |
| description | It is widely acknowledged that digital transformation provides the opportunity for business process improvements. We can see many businesses, specifically in Indonesia's private and public sectors, leveraging technology to provide better service to their stakeholders. This research seeks insight into stakeholders’ engagement in digital transformation in the Indonesian healthcare system, namely the National Health Security Mobile application (Mobile JKN). This study employs a quantitative method to analyze user sentiment from Google Play reviews. Firstly, user reviews are extracted, preprocessing steps are applied, and machine learning-based sentiment labeling is employed afterward to categorize them into positive, neutral, and negative sentiments. The machine labeling process is carried out in document-level analysis, meaning user reviews represent their overall sentiment. Subsequently, we study the most prominent words using Word Cloud to determine the topics mainly discussed in positive and negative sentiments. The result is that 56.10% of reviews from 7 June 2016 to 14 July 2024 contain positive sentiments, while 43.17% contain negative sentiments. Neutral reviews contribute the most minor proportion, making up only 0.73%. The most prominent words in positive sentiment reviews, such as easy, sound, and helpful, suggest the user perception of the National Health Security Mobile as being easy to use and successfully accommodating the user's needs. In contrast, the words application, update, complex, login, code, verification, register, open, use, and error dominate the negative reviews, indicating users had difficulties logging in and registering user accounts, mainly related to frequent updates and Time Password errors. |
| format | Article |
| id | doaj-art-ea8ac8f33dd64cb385197bbef9716242 |
| institution | DOAJ |
| issn | 2798-7183 2798-6705 |
| language | Indonesian |
| publishDate | 2024-12-01 |
| publisher | BPJS Kesehatan |
| record_format | Article |
| series | Jurnal Jaminan Kesehatan Nasional |
| spelling | doaj-art-ea8ac8f33dd64cb385197bbef97162422025-08-20T02:51:16ZindBPJS KesehatanJurnal Jaminan Kesehatan Nasional2798-71832798-67052024-12-014217618810.53756/jjkn.v4i2.269269Sentiment Analysis of National Health Security Mobile Application Review Using Machine LearningDeta Novian Anantika PutraIt is widely acknowledged that digital transformation provides the opportunity for business process improvements. We can see many businesses, specifically in Indonesia's private and public sectors, leveraging technology to provide better service to their stakeholders. This research seeks insight into stakeholders’ engagement in digital transformation in the Indonesian healthcare system, namely the National Health Security Mobile application (Mobile JKN). This study employs a quantitative method to analyze user sentiment from Google Play reviews. Firstly, user reviews are extracted, preprocessing steps are applied, and machine learning-based sentiment labeling is employed afterward to categorize them into positive, neutral, and negative sentiments. The machine labeling process is carried out in document-level analysis, meaning user reviews represent their overall sentiment. Subsequently, we study the most prominent words using Word Cloud to determine the topics mainly discussed in positive and negative sentiments. The result is that 56.10% of reviews from 7 June 2016 to 14 July 2024 contain positive sentiments, while 43.17% contain negative sentiments. Neutral reviews contribute the most minor proportion, making up only 0.73%. The most prominent words in positive sentiment reviews, such as easy, sound, and helpful, suggest the user perception of the National Health Security Mobile as being easy to use and successfully accommodating the user's needs. In contrast, the words application, update, complex, login, code, verification, register, open, use, and error dominate the negative reviews, indicating users had difficulties logging in and registering user accounts, mainly related to frequent updates and Time Password errors.https://jurnal-jkn.bpjs-kesehatan.go.id/index.php/jjkn/article/view/269sentiment analysislogistic modelsmachine learningapplication review |
| spellingShingle | Deta Novian Anantika Putra Sentiment Analysis of National Health Security Mobile Application Review Using Machine Learning Jurnal Jaminan Kesehatan Nasional sentiment analysis logistic models machine learning application review |
| title | Sentiment Analysis of National Health Security Mobile Application Review Using Machine Learning |
| title_full | Sentiment Analysis of National Health Security Mobile Application Review Using Machine Learning |
| title_fullStr | Sentiment Analysis of National Health Security Mobile Application Review Using Machine Learning |
| title_full_unstemmed | Sentiment Analysis of National Health Security Mobile Application Review Using Machine Learning |
| title_short | Sentiment Analysis of National Health Security Mobile Application Review Using Machine Learning |
| title_sort | sentiment analysis of national health security mobile application review using machine learning |
| topic | sentiment analysis logistic models machine learning application review |
| url | https://jurnal-jkn.bpjs-kesehatan.go.id/index.php/jjkn/article/view/269 |
| work_keys_str_mv | AT detanoviananantikaputra sentimentanalysisofnationalhealthsecuritymobileapplicationreviewusingmachinelearning |