Sentiment Analysis for Online Learning using the Lexicon-Based Method and the Support Vector Machine Algorithm
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Sentiment Analysis for Online Learning using the Lexicon-Based Method and the Support Vector Machine Algorithm |
| 2. | Creator | Author's name, affiliation, country | M. Khairul Anam; STMIK Amik Riau; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Triyani Arita Fitri; STMIK Amik Riau; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Agustin Agustin; STMIK Amik Riau; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Lusiana Lusiana; STMIK Amik Riau; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Muhammad Bambang Firdaus; Universitas Mulawarman; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Agus Tri Nurhuda; STMIK Amik Riau; Indonesia |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Lexicon Based; Online Learning; Sentiment Analysis; Support Vector Machine |
| 4. | Description | Abstract | The pros and cons regarding online learning has been a hot topic in society, both on social media and in the real world. Indonesian netizens still post opinions about online learning on social media such as Twitter. This study aims to analyze public comments to determine whether the trend of the comments is positive, negative, or neutral. The classification of netizen opinions is called sentiment analysis. This study applies 2 ways of carrying out sentiment analysis. The first stage employs the SVM algorithm with data labeling automatically obtained from the Emprit Academy drone portal while the second stage is still using the SVM algorithm but the data labeling with lexicon-based method. The results of this study are comparisons of labels obtained automatically from the Emprit Academy drone portal and labeling using lexicon based. The SVM algorithm obtains an accuracy of 90%, while the use of lexicon-based increases the accuracy value by 5% to 95%. It can be concluded that labeling data using a lexicon-based method can improve the accuracy of the SVM algorithm. |
| 5. | Publisher | Organizing agency, location | Prodi Teknik Informatika FIK Universitas Muslim Indonesia |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2023-08-16 |
| 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/1590 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.33096/ilkom.v15i2.1590.290-302 |
| 11. | Source | Title; vol., no. (year) | ILKOM Jurnal Ilmiah; Vol 15, No 2 (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 M. Khairul anam, Triyani Arita Fitri, Triyani Arita Fitri, Agustin Agustin, Agustin Agustin, Lusiana Lusiana, Lusiana Lusiana, Muhammad Bambang Firdaus, Muhammad Bambang Firdaus, Agus Tri Nurhuda, Agus Tri Nurhuda This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |