Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale
Al-Ghiff Steak is a restaurant located in Cirebon City that offers quality steaks at affordable prices. For maintaining a competitive Al-Ghiff Steak advantage and reputation, it is important to build a good relationship with customers and have a business strategy that considers customer opinions. H...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Brawijaya
2021-12-01
|
| Series: | JITeCS (Journal of Information Technology and Computer Science) |
| Online Access: | https://jitecs.ub.ac.id/index.php/jitecs/article/view/330 |
| _version_ | 1797248829521657856 |
|---|---|
| author | Novira Azpiranda Ahmad Afif Supianto Nanang Yudi Setiawan Endang Suryawati R. Sandra Yuwana Arafat Febriandirza |
| author_facet | Novira Azpiranda Ahmad Afif Supianto Nanang Yudi Setiawan Endang Suryawati R. Sandra Yuwana Arafat Febriandirza |
| author_sort | Novira Azpiranda |
| collection | DOAJ |
| description |
Al-Ghiff Steak is a restaurant located in Cirebon City that offers quality steaks at affordable prices. For maintaining a competitive Al-Ghiff Steak advantage and reputation, it is important to build a good relationship with customers and have a business strategy that considers customer opinions. However, in its implementation, Al-Ghiff Steak has difficulty when collecting and processing customer review data manually. Therefore, it is necessary to conduct sentiment analysis by utilizing Google Reviews to determine customer perspectives regarding Al-Ghiff Steak products and services. This analysis was conducted on 968 Google Review reviews from 2016 to 2020 using the Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Classification testing is done with a confusion matrix against four parameters: accuracy, precision, recall, and f1-score. SVM with TF-IDF gets accuracy value 83%, precision 64%, recall 60% and f1-score 59%. The sentiment classification result is then visualized in the form of a dashboard. We utilize the System Usability Scale (SUS) for usability testing, which produces a value of 77.5. This result achieve the Acceptable category and an Excellent rating.
|
| first_indexed | 2024-04-24T20:20:48Z |
| format | Article |
| id | doaj.art-9fd1321ab98c4ae58ec488aeecf9aa76 |
| institution | Directory Open Access Journal |
| issn | 2540-9433 2540-9824 |
| language | English |
| last_indexed | 2024-04-24T20:20:48Z |
| publishDate | 2021-12-01 |
| publisher | University of Brawijaya |
| record_format | Article |
| series | JITeCS (Journal of Information Technology and Computer Science) |
| spelling | doaj.art-9fd1321ab98c4ae58ec488aeecf9aa762024-03-22T08:31:54ZengUniversity of BrawijayaJITeCS (Journal of Information Technology and Computer Science)2540-94332540-98242021-12-016310.25126/jitecs.202163330Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability ScaleNovira Azpiranda0Ahmad Afif Supianto1Nanang Yudi Setiawan2Endang Suryawati3R. Sandra Yuwana4Arafat Febriandirza5Brawijaya University, Malang, IndonesiaBrawijaya University Malang, National Research and Innovation Agency, Bandung, IndonesiaBrawijaya University, Malang, IndonesiaNational Research and Innovation Agency, Bandung, IndonesiaNational Research and Innovation Agency, Bandung, IndonesiaNational Research and Innovation Agency, Bandung, Indonesia Al-Ghiff Steak is a restaurant located in Cirebon City that offers quality steaks at affordable prices. For maintaining a competitive Al-Ghiff Steak advantage and reputation, it is important to build a good relationship with customers and have a business strategy that considers customer opinions. However, in its implementation, Al-Ghiff Steak has difficulty when collecting and processing customer review data manually. Therefore, it is necessary to conduct sentiment analysis by utilizing Google Reviews to determine customer perspectives regarding Al-Ghiff Steak products and services. This analysis was conducted on 968 Google Review reviews from 2016 to 2020 using the Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Classification testing is done with a confusion matrix against four parameters: accuracy, precision, recall, and f1-score. SVM with TF-IDF gets accuracy value 83%, precision 64%, recall 60% and f1-score 59%. The sentiment classification result is then visualized in the form of a dashboard. We utilize the System Usability Scale (SUS) for usability testing, which produces a value of 77.5. This result achieve the Acceptable category and an Excellent rating. https://jitecs.ub.ac.id/index.php/jitecs/article/view/330 |
| spellingShingle | Novira Azpiranda Ahmad Afif Supianto Nanang Yudi Setiawan Endang Suryawati R. Sandra Yuwana Arafat Febriandirza Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale JITeCS (Journal of Information Technology and Computer Science) |
| title | Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale |
| title_full | Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale |
| title_fullStr | Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale |
| title_full_unstemmed | Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale |
| title_short | Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale |
| title_sort | sentiment anlysis on customer reviews using support vector machine and usability scoring using system usability scale |
| url | https://jitecs.ub.ac.id/index.php/jitecs/article/view/330 |
| work_keys_str_mv | AT noviraazpiranda sentimentanlysisoncustomerreviewsusingsupportvectormachineandusabilityscoringusingsystemusabilityscale AT ahmadafifsupianto sentimentanlysisoncustomerreviewsusingsupportvectormachineandusabilityscoringusingsystemusabilityscale AT nanangyudisetiawan sentimentanlysisoncustomerreviewsusingsupportvectormachineandusabilityscoringusingsystemusabilityscale AT endangsuryawati sentimentanlysisoncustomerreviewsusingsupportvectormachineandusabilityscoringusingsystemusabilityscale AT rsandrayuwana sentimentanlysisoncustomerreviewsusingsupportvectormachineandusabilityscoringusingsystemusabilityscale AT arafatfebriandirza sentimentanlysisoncustomerreviewsusingsupportvectormachineandusabilityscoringusingsystemusabilityscale |