Full Description
| Cataloguing Source | LibUI ind rda |
| Content Type | text (rdacontent) |
| Media Type | computer (rdamedia) |
| Carrier Type | online resource (rdacarrier) |
| Physical Description | xii, 71 pages : illustration ; appendix |
| Concise Text | |
| Holding Institution | Universitas Indonesia |
| Location | Perpustakaan UI |
- Availability
- Digital Files: 1
- Review
- Cover
- Abstract
| Call Number | Barcode Number | Availability |
|---|---|---|
| S-Pdf | 14-20-326003112 | TERSEDIA |
| No review available for this collection: 20473294 |
Abstract
Penelitian ini mengembangkan dan menganalisa sistem pendeteksi plagiarisme dua bahasa berbasis Latent Semantic Analysis untuk karya tulis berbahasa Indonesia dan referensi berbahasa Inggris. Sistem pendeteksi plagiarisme menggunakan algoritma backpropagation neural network untuk melakukan klasifikasi pasangan karya tulis berbahasa Indonesia dan Inggris yang sudah dinilai tingkatan plagiarismenya secara manual. Sistem dapat memperoleh klasifikasi akurasi F-measure sampai dengan 92.75.
Hasil percobaan menunjukkan bahwa akurasi tertinggi dapat diperoleh jika menggunakan metode term frequency binary dalam penghitungan jumlah kata dan penggunaan frobenius norm, vector angle slice, dan vector angle pad sebagai pilihan fitur untuk masukan backpropagation neural network.
This research aims to develop and analyse dual language plagiarism detection system based on Latent Semantic Analysis for papers with Indonesian language and reference text with English language. The plagiarism detection system uses backpropagation neural network algorithm to classify pairs of Indonesian and English papers which plagiarism levels has been graded manually. The system has reached classification accuracy using F measure metric up to 92.75. Experiment results show that the highest accuracy obtained when using term frequency binary method in counting frequency of words and using frobenius norm, vector angle slice, and vector angle pad features for backpropagtion neural network input.
This research aims to develop and analyse dual language plagiarism detection system based on Latent Semantic Analysis for papers with Indonesian language and reference text with English language. The plagiarism detection system uses backpropagation neural network algorithm to classify pairs of Indonesian and English papers which plagiarism levels has been graded manually. The system has reached classification accuracy using F measure metric up to 92.75. Experiment results show that the highest accuracy obtained when using term frequency binary method in counting frequency of words and using frobenius norm, vector angle slice, and vector angle pad features for backpropagtion neural network input.