Implementasi Algoritma Support Vector Machine dan Chi Square untuk Analisis Sentimen User Feedback Aplikasi Article Swipe
Lulu Luthfiana
,
Julio Christian Young
,
Andre Rusli
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.31937/ti.v12i2.1828
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.31937/ti.v12i2.1828
In order to adapt with evolving requirements and perform continuous software maintenance, it is essential for the software developers to understand the content of user feedback. User feedback such as bug report could provide so much information regarding the product from user's point of view, especially parts that need improvements. However, it is often difficult to read all the feedback for products with enormous number of users as manually reading and analyzing each feedback could take too much time and effort. This research aims to develop a model for automatic feedback classification by implementing Support Vector Machine for the classifier's algorithm and Chi-square method for feature selection. The model is developed using Python programming language and is then evaluated under different scenarios in order to measure its performance. Using a ratio of training and testing set of 80:20, our model achieved 77% accuracy, 50% precision, 55% recall, and 73% F1-score with 6.63 critical value and C=100 and gamma 0.001 as the SVM hyperparameters.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.31937/ti.v12i2.1828
- https://ejournals.umn.ac.id/index.php/TI/article/download/1828/1060
- OA Status
- diamond
- Cited By
- 8
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3107971001
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3107971001Canonical identifier for this work in OpenAlex
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https://doi.org/10.31937/ti.v12i2.1828Digital Object Identifier
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Implementasi Algoritma Support Vector Machine dan Chi Square untuk Analisis Sentimen User Feedback AplikasiWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-12-29Full publication date if available
- Authors
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Lulu Luthfiana, Julio Christian Young, Andre RusliList of authors in order
- Landing page
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https://doi.org/10.31937/ti.v12i2.1828Publisher landing page
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https://ejournals.umn.ac.id/index.php/TI/article/download/1828/1060Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ejournals.umn.ac.id/index.php/TI/article/download/1828/1060Direct OA link when available
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Computer science, Support vector machine, Hyperparameter, Machine learning, Python (programming language), Data mining, Artificial intelligence, Software, Classifier (UML), Feature vector, Programming languageTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
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12Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy, | 142 |
| abstract_inverted_index.algorithm | 100 |
| abstract_inverted_index.analyzing | 71 |
| abstract_inverted_index.automatic | 89 |
| abstract_inverted_index.developed | 110 |
| abstract_inverted_index.different | 120 |
| abstract_inverted_index.difficult | 54 |
| abstract_inverted_index.essential | 14 |
| abstract_inverted_index.evaluated | 118 |
| abstract_inverted_index.feedback. | 25 |
| abstract_inverted_index.regarding | 37 |
| abstract_inverted_index.scenarios | 121 |
| abstract_inverted_index.Chi-square | 102 |
| abstract_inverted_index.continuous | 9 |
| abstract_inverted_index.developers | 18 |
| abstract_inverted_index.especially | 45 |
| abstract_inverted_index.precision, | 144 |
| abstract_inverted_index.selection. | 106 |
| abstract_inverted_index.understand | 20 |
| abstract_inverted_index.information | 36 |
| abstract_inverted_index.programming | 113 |
| abstract_inverted_index.classifier's | 99 |
| abstract_inverted_index.implementing | 93 |
| abstract_inverted_index.maintenance, | 11 |
| abstract_inverted_index.performance. | 127 |
| abstract_inverted_index.requirements | 6 |
| abstract_inverted_index.improvements. | 49 |
| abstract_inverted_index.classification | 91 |
| abstract_inverted_index.hyperparameters. | 162 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.84352346 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |