Text classification to predict skin concerns over skincare using bidirectional mechanism in long short-term memory
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Text classification to predict skin concerns over skincare using bidirectional mechanism in long short-term memory |
| 2. | Creator | Author's name, affiliation, country | Devi Fitrianah; Bina Nusantara University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Andre Hangga Wangsa; Universitas Mercu Buana; Indonesia |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Dermatology; Multi-class text classification deep learning; Natural language processing; Skincare |
| 4. | Description | Abstract | There are numerous types of skincare, each with its own set of benefits based on key ingredients. This may be difficult for beginners who are purchasing skincare for the first time due to a lack of knowledge about skincare and their own skin concerns. Hence, based on this problem, it is possible to find out the right skin concern that can be handled in each skincare product automatically by multi-class text classification. The purpose of this research is to build a deep learning model capable of predicting skin concerns that each skincare product can treat. By comparing the performance and results of predicting the correct skin condition for each skincare product description using both long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), The best results are given by Bi-LSTM, which has an accuracy score of 98.04% and a loss score of 19.19%. Meanwhile, LSTM results have an accuracy score of 94.12% and a loss score of 19.91%. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | Devi Fitrianah, Bina Nusantara University |
| 7. | Date | (YYYY-MM-DD) | 2022-11-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://iaesprime.com/index.php/csit/article/view/194 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.11591/csit.v3i3.p137-147 |
| 11. | Source | Title; vol., no. (year) | Computer Science and Information Technologies; Vol 3, No 3: November 2022 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2022 Andre Hangga Wangsa, Devi Fitrianah This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |