WIDJAYA, JULIUS ALVIN (2022) SENTIMENT ANALYSIS ON HOMESCHOOLING TOPIC USING LONG SHORT TERM MEMORY AND SUPPORT VECTOR MACHINE. Other thesis, Universitas Katholik Soegijapranata Semarang.
|
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-COVER_a.pdf Download (290kB) | Preview |
|
|
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-BAB I_a.pdf Download (217kB) | Preview |
|
![[img]](https://repository.unika.ac.id/style/images/fileicons/text.png) |
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-BAB II_a.pdf Restricted to Registered users only Download (188kB) |
|
|
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-BAB III_a.pdf Download (398kB) | Preview |
|
|
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-BAB IV_a.pdf Download (219kB) | Preview |
|
|
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-BAB V_a.pdf Download (292kB) | Preview |
|
|
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-BAB VI_a.pdf Download (207kB) | Preview |
|
|
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-DAPUS_a.pdf Download (208kB) | Preview |
|
|
Text
18.K1.0020-JULIUS ALVIN WIDJAYA-LAMP_a.pdf Download (271kB) | Preview |
Abstract
Social media is a medium that can be used to share information. In addition, social media is also a suitable place to see the conditions that exist in society. Besides being useful as a medium for sharing information, social media is also often used to convey public opinion or sentiment on a topic being discussed. To find out sentiment or opinion on a topic, this research uses the Sentiment Analysis method. Sentiment Analysis can be classified well using several models such as Deep Learning and Supervised Learning. The Deep Learning model that will be used in this research is the Long Short Term Memory method, and the Supervised Learning model that will be used in this case is the Support Vector Machine method. The data used is topic data about English Homeschooling taken from Twitter. In this study, the Long Short Term Memory method will be compared with the Support Vector Machine method based on the results of accuracy, precision, recall and F1-Score obtained. Based on the test results using the Long Short-Term Memory and Support Vector Machine methods, it is found that the Long Short Term Memory method is superior with an Accuracy value of 83.8%, Precision 88.2%, Recall 92.3%, and F1-Score 90.2% compared to the results of the Support Vector Machine method with an accuracy value of 71%, Precision 83.9%, Recall 79.6%, and F1-Score 81.7%.
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | mr AM. Pudja Adjie Sudoso |
| Date Deposited: | 26 Oct 2022 09:24 |
| Last Modified: | 26 Oct 2022 09:24 |
| URI: | http://repository.unika.ac.id/id/eprint/30025 |
Actions (login required)
 |
View Item |
