Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm
Radical content in procedural meaning is content which have provoke the violence, spread the hatred and anti nationalism. Radical definition for each country is different, especially in Indonesia. Radical content is more identical with provocation issue, ethnic and religious hatred that is called SA...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Politeknik Elektronika Negeri Surabaya
2018-01-01
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| Series: | Emitter: International Journal of Engineering Technology |
| Subjects: | |
| Online Access: | https://emitter.pens.ac.id/index.php/emitter/article/view/214 |
| _version_ | 1818886299934785536 |
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| author | Muh Subhan Amang Sudarsono Ali Ridho Barakbah |
| author_facet | Muh Subhan Amang Sudarsono Ali Ridho Barakbah |
| author_sort | Muh Subhan |
| collection | DOAJ |
| description | Radical content in procedural meaning is content which have provoke the violence, spread the hatred and anti nationalism. Radical definition for each country is different, especially in Indonesia. Radical content is more identical with provocation issue, ethnic and religious hatred that is called SARA in Indonesian languange. SARA content is very difficult to detect due to the large number, unstructure system and many noise can be caused multiple interpretations. This problem can threat the unity and harmony of the religion. According to this condition, it is required a system that can distinguish the radical content or not. In this system, we propose text mining approach using DF threshold and Human Brain as the feature extraction. The system is divided into several steps, those are collecting data which is including at preprocessing part, text mining, selection features, classification for grouping the data with class label, simillarity calculation of data training, and visualization to the radical content or non radical content. The experimental result show that using combination from 10-cross validation and k-Nearest Neighbor (kNN) as the classification methods achieve 66.37% accuracy performance with 7 k value of kNN method[1]. |
| first_indexed | 2024-12-19T16:19:08Z |
| format | Article |
| id | doaj.art-3cc1d4fb71ac49a48bbf532613775393 |
| institution | Directory Open Access Journal |
| issn | 2355-391X 2443-1168 |
| language | English |
| last_indexed | 2024-12-19T16:19:08Z |
| publishDate | 2018-01-01 |
| publisher | Politeknik Elektronika Negeri Surabaya |
| record_format | Article |
| series | Emitter: International Journal of Engineering Technology |
| spelling | doaj.art-3cc1d4fb71ac49a48bbf5326137753932022-12-21T20:14:31ZengPoliteknik Elektronika Negeri SurabayaEmitter: International Journal of Engineering Technology2355-391X2443-11682018-01-015210.24003/emitter.v5i2.21493Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor AlgorithmMuh Subhan0Amang Sudarsono1Ali Ridho Barakbah2Electronics Engineering Polytechnic Institute of SurabayaElectronics Engineering Polytechnic Institute of SurabayaElectronics Engineering Polytechnic Institute of SurabayaRadical content in procedural meaning is content which have provoke the violence, spread the hatred and anti nationalism. Radical definition for each country is different, especially in Indonesia. Radical content is more identical with provocation issue, ethnic and religious hatred that is called SARA in Indonesian languange. SARA content is very difficult to detect due to the large number, unstructure system and many noise can be caused multiple interpretations. This problem can threat the unity and harmony of the religion. According to this condition, it is required a system that can distinguish the radical content or not. In this system, we propose text mining approach using DF threshold and Human Brain as the feature extraction. The system is divided into several steps, those are collecting data which is including at preprocessing part, text mining, selection features, classification for grouping the data with class label, simillarity calculation of data training, and visualization to the radical content or non radical content. The experimental result show that using combination from 10-cross validation and k-Nearest Neighbor (kNN) as the classification methods achieve 66.37% accuracy performance with 7 k value of kNN method[1].https://emitter.pens.ac.id/index.php/emitter/article/view/214K-NNNearest NeighbourRadical ContentIndonesia |
| spellingShingle | Muh Subhan Amang Sudarsono Ali Ridho Barakbah Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm Emitter: International Journal of Engineering Technology K-NN Nearest Neighbour Radical Content Indonesia |
| title | Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm |
| title_full | Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm |
| title_fullStr | Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm |
| title_full_unstemmed | Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm |
| title_short | Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm |
| title_sort | classification of radical web content in indonesia using web content mining and k nearest neighbor algorithm |
| topic | K-NN Nearest Neighbour Radical Content Indonesia |
| url | https://emitter.pens.ac.id/index.php/emitter/article/view/214 |
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