User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm
On the Google Play Store, users often read other users' app reviews and reputations, before downloading an app. This makes the analysis of user reviews very interesting for app owners to make future decisions. This study aims to analyze user reviews of the Info BMKG application on the Google Pl...
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| Format: | Article |
| Language: | English |
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Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat
2023-06-01
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| Series: | Inspiration |
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| Online Access: | https://ojs.unitama.ac.id/index.php/inspiration/article/view/42 |
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| author | Ilham Fannani Enggar Novianto Alfin Syarifuddin Syahab |
| author_facet | Ilham Fannani Enggar Novianto Alfin Syarifuddin Syahab |
| author_sort | Ilham Fannani |
| collection | DOAJ |
| description | On the Google Play Store, users often read other users' app reviews and reputations, before downloading an app. This makes the analysis of user reviews very interesting for app owners to make future decisions. This study aims to analyze user reviews of the Info BMKG application on the Google Play Store, using sentiment analysis. This user review analysis uses the Support Vector Machine (SVM) method. The evaluation proposal was made from more than 3,000 user reviews collected from the INFOBMKG application on the Google Play Store. The results of the analysis using the Support Vector Machine produce an accuracy of 85.54 % and the most frequently reviewed positive review results are "Good", while the most frequently reviewed negative reviews are "Error". Which indicates a complaint against INFOBMKG users, and from the negative words that appear most often, there are two combinations of the two words that appear most often together, namely the word "very helpful" and the word "less accurate", which indicates that user often complain about problems related to application performance. The results of the sentiment analysis process of testing 3000 review data using the fold = 5 test value in the Support Vector Machine (SVM) method obtained an accuracy of 85.54 % which produces predictions on data testing, namely 1500 positive reviews and 1500 negative reviews 1500 reviews. |
| format | Article |
| id | doaj-art-93605149b5194077a8e42b1ce538b10f |
| institution | Kabale University |
| issn | 2088-6705 2621-5608 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat |
| record_format | Article |
| series | Inspiration |
| spelling | doaj-art-93605149b5194077a8e42b1ce538b10f2025-01-28T05:36:06ZengUniversitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian MasyarakatInspiration2088-67052621-56082023-06-01131485810.35585/inspir.v13i1.4242User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine AlgorithmIlham Fannani0Enggar Novianto1Alfin Syarifuddin Syahab2Universitas Teknologi YogyakartaUniversitas Teknologi YogyakartaUniversitas Teknologi YogyakartaOn the Google Play Store, users often read other users' app reviews and reputations, before downloading an app. This makes the analysis of user reviews very interesting for app owners to make future decisions. This study aims to analyze user reviews of the Info BMKG application on the Google Play Store, using sentiment analysis. This user review analysis uses the Support Vector Machine (SVM) method. The evaluation proposal was made from more than 3,000 user reviews collected from the INFOBMKG application on the Google Play Store. The results of the analysis using the Support Vector Machine produce an accuracy of 85.54 % and the most frequently reviewed positive review results are "Good", while the most frequently reviewed negative reviews are "Error". Which indicates a complaint against INFOBMKG users, and from the negative words that appear most often, there are two combinations of the two words that appear most often together, namely the word "very helpful" and the word "less accurate", which indicates that user often complain about problems related to application performance. The results of the sentiment analysis process of testing 3000 review data using the fold = 5 test value in the Support Vector Machine (SVM) method obtained an accuracy of 85.54 % which produces predictions on data testing, namely 1500 positive reviews and 1500 negative reviews 1500 reviews.https://ojs.unitama.ac.id/index.php/inspiration/article/view/42text miningsentiment analysissupport vector machine |
| spellingShingle | Ilham Fannani Enggar Novianto Alfin Syarifuddin Syahab User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm Inspiration text mining sentiment analysis support vector machine |
| title | User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm |
| title_full | User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm |
| title_fullStr | User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm |
| title_full_unstemmed | User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm |
| title_short | User Analysis of Info BMKG Application in The Perspective of Human Computer Interaction Using Support Vector Machine Algorithm |
| title_sort | user analysis of info bmkg application in the perspective of human computer interaction using support vector machine algorithm |
| topic | text mining sentiment analysis support vector machine |
| url | https://ojs.unitama.ac.id/index.php/inspiration/article/view/42 |
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