DETEKSI TINGKAT KEMIRIPAN DATA SISWA PESERTA UJIAN BERBASIS KOMPUTER PADA BIMBINGAN BELAJAR EXPERT DENGAN MENGGUNAKAN METODE LEVENSHTEIN DISTANCE
DOI:
https://doi.org/10.30829/algoritma.v1i01.1305Abstract
In the world of education evaluation is mandatory to measure the level of success educational process. Computer-based Test often make students fail because of incorrect filling of biodata. This research to measure the level of similarity student identity so small mistake made by participants can be tolerated by the computer when filling of biodata and ultimately can reduce the failure rate of computer-based Test participants. This research is very important considering the change of Study methods this time to using of technology. By using approach of Levenshtein Distance method can detect the resemblance of biodata of exam participants so that when seen there is similarity it can improve the writing of wrong data. By implementing this method into the problem correction application according to the needs of the system in Bimbel Expert.Keywords : Levenstain Distance, Similarity Of Data, Examination.References
Februariyanti, Herny. 2013. Perancangan Pengindeks Kata pada Dokumen Teks Menggunakan Aplikasi Berbasis Web. Jurnal Teknologi Informasi DINAMIK Volume 18 Nomor 2, Program Studi Sistem Informasi, Universitas Stikubank.
Hultberg, J. dan J.P. Helger. 2007. Seminar Course in Algorithms - Project Report.
Junedy, Richard. 2014. Perancangan Aplikasi Deteksi Kemiripan Isi Dokumen Teks dengan Menggunakan Metode Leveshtein Distance. Jurnal Pelita Informatika Budi Darma Vol. VII No.2, Jurusan Teknik Informatika, STMIK Budi Darma, Medan.
Pratama B. P., S. A. Pamungkas. 2016. Analisis kinerja algoritma levenshtein distance dalam mendeteksi kemiripan dokumen teks. Jurnal "LOG!K@" , Jilid 6, No. 2
Sjukani, Moh. 2013. Algoritma (Algoritma dan Struktur Data 1) dengan C, C++, dan Java. Jakarta: Mitra Wacana Media.
Winarsono, D., D.O. Siahaan dan U. Yuhana. 2009. Sistem Penilaian Otomatis Kemiripan Kalimat Menggunakan Syntactic Semantic Similarity pada Sistem ELearning. Jurnal Ilmiah KURSOR Menuju Solusi Teknologi Informasi Volume 5 Nomor 2, Jurusan Teknik Informatika, ITS, Surabaya.
Zhan Su, Byung-Ryul Ahn, Ki-yol Eom, Min-koo Kang, Jin-Pyung Kim, dan MoonKyun Kim. 2008. Plagiarism Detection Using the Levenshtein Distance and SmithWaterman Algorithm. The 3rd Intetnational Conference on Innovative Computing Information and Control, Department of Artificial Intelligence, University of Sungkyunkwan, Cheoncheon dong, Jangan-gu, Suwon, Korea. Diakses tanggal 12 mei 2017, dari http://ieeexplore.ieee.org.
Downloads
Published
Issue
Section
License
Copyright
Authors published in this journal agree to the following terms:
The copyright of each article remains with the author
The authors grant the journal first publication rights with the work simultaneously licensed under the Creative Commons Attribution License, which allows others to share the work with acknowledgment of authorship and initial publication in this journal.
Authors may enter into separate additional contractual agreements for non-exclusive distribution of the journal's published version of the work (e.g., submitting it to an institutional repository or publishing it in a book), with acknowledgment of their initial publication in this journal.
Authors are permitted and encouraged to post their work online (E.g. in an Institutional Repository or on their website) before and during the submission process, as this can lead to productive exchange, as well as citing earlier and larger published works.
The article and all related materials are published under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
This is a summary that the reader's license allows (and is not a replacement). Disclaimer.
You are free to:
Share — copying and redistributing material in any medium or format
Adapt — remix, change, and build on the material for any purpose, even commercial.
The licensor cannot take away this freedom as long as you follow the terms of the license.
Under the following conditions:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, modify, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything under the license.