Sentiment Analysis of Shopee App Reviews Using Random Forest and Support Vector Machine
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Sentiment Analysis of Shopee App Reviews Using Random Forest and Support Vector Machine |
| 2. | Creator | Author's name, affiliation, country | Suswadi Suswadi; Universitas Tunas Pembangunan Surakarta; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Moh. Erkamim; Universitas Tunas Pembangunan Surakarta; Indonesia |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Covid-19; Random Forest; Sentiment Analysis; Shopee; Support Vector Machine |
| 4. | Description | Abstract | During the COVID-19 outbreak, Indonesian marketplaces were significantly impacted including Shopee app. It is necessary to evaluate the features and services of the Shopee application by looking at the feedback given by the public in Google Play Store reviews. This is what prompted research to be conducted from Kaggle data in the form of Shopee reviews. From this data, sentiment analysis is carried out utilizing the Support Vector Machine (SVM) and Random Forest methods. This method are used to classify reviews based on positive and negative sentiments. The results showed that the level of classification accuracy in the Random Forest model is 82.21%. While the SVM model provides a higher level of accuracy of 84.71%. Data exploration on positive and negative sentiment classes is used to find insight into this problem. In positive sentiment, words that often appear such as “belanja”, “aplikasi”, and “barang” are found. As for the negative sentiments, namely “ongkir”, “kirim”, “aplikasi”. These words can be used to be a quality improvement or evaluation for the Shopee company. |
| 5. | Publisher | Organizing agency, location | Prodi Teknik Informatika FIK Universitas Muslim Indonesia |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2023-12-20 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1610 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.33096/ilkom.v15i3.1610.427-435 |
| 11. | Source | Title; vol., no. (year) | ILKOM Jurnal Ilmiah; Vol 15, No 3 (2023) |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2023 Suswandi, Moh Erkamim This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |