Sentiment Analysis and Classification of Forest Fires in Indonesia
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Sentiment Analysis and Classification of Forest Fires in Indonesia |
| 2. | Creator | Author's name, affiliation, country | Indra Irawanto; AMIKOM Yogyakarta of University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Cynthia Widodo; AMIKOM Yogyakarta of University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Atin Hasanah; AMIKOM Yogyakarta of University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Prema Adhitya Dharma Kusumah; AMIKOM Yogyakarta of University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Kusirini Kusrini; AMIKOM Yogyakarta of University; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Kusnawi Kusnawi; AMIKOM Yogyakarta of University; Indonesia |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Sentiment Analysis; Forest fires; Naive Bayes; Random Forest; SVM (Support Vector Machine). |
| 4. | Description | Abstract | Twitter is a well-known social media platform since it allows users to retweet, leave comments, exchange the latest information, and even find out about forest fires. However, no one has processed Twitter data in the form of the topic of forest fires. Despite the fact that this information is incredibly important for determining how much people care about sharing this knowledge and this phenomenon. Hence, one of the efforts in managing Twitter data in the form of text is using NLP (Natural Language Processing) which is now starting to be widely discussed. In addition, the use of word weighting utilizing Vader will also be used in this process. Furthermore, the use classifying process is conducted using 3 kinds of algorithms including Naïve Bayes, Random Forest and SVM (Support Vector Machine). The results of this study, the accuracy obtained from each method has not reached 90%. The Precision, Recall and F1-Score values have also not reached 90%. |
| 5. | Publisher | Organizing agency, location | Prodi Teknik Informatika FIK Universitas Muslim Indonesia |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2023-04-07 |
| 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/1337 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.33096/ilkom.v15i1.1337.175-185 |
| 11. | Source | Title; vol., no. (year) | ILKOM Jurnal Ilmiah; Vol 15, No 1 (2023) |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files |
Hasil Turnitin (1MB) data set (135KB) hasil pelabelan vader lexicon (70KB) |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2023 atin hasanah, Indra Irawanto, Cynthia Widodo, Prema Kusumah, Kusrini Kusrini, Muhammad Kusnawi This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |