Deteksi Plagiarisme Skripsi Mahasiswa dengan Metode Single-link Clustering dan Jaro-Winkler Distance



Abstract— The rise of plagiarism is one of the negative impacts of the development of information and communication technology. Plagiarism can occur anywhere. One of the examples is a university with the object of plagiarism as a student's final project. So we need a system to detect plagiarism so that it can suppress plagiarism in the college environment. In detecting the similarity of a writing will be faster if the writing has been grouped before compared to each other. Single-link clustering was chosen because it has a simple algorithm and can be implemented without the initial cluster. In plagiarism plagiarism usually changes the sentence structure so that it looks different Jaro-Winkler distance is chosen because it can detect similarities in paragraphs that have been changed in sentence structure because Jaro-Winkler distance has a flexible indexing with theoretical distance so that a word or character is considered the same. The stages in this study include data collection, preprocessing, grouping writing with Single-link clustering, comparing writing with jaro-winkler distance, and testing with precision and recall. After testing, the average value of precision was 84.37% and recall was 84.37% with a level of plagiarism of 99.1%. Keywords—: jaro-winkler distance; plagiarisme; single-link clustering.


Download data is not yet available.

Arinda, F.P. (2015). Ketidakjujran Akademik Mahasiswa Perguruan Tinggi X di Surakarta. J. Appl. Microbiology., vol. 119, no. 3, 859-867.

Jaro, M. A. (1989). Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. Journal of the American Statistical Association, vol. 84, no. 406, 414–420

Kurniawati, A. (2010). Implementasi Algoritma Jaro-Winkler Distance untuk Membandingkan Kesamaan Dokumen Berbahasa Indonesia. In Seminar Nasional Ilmu Komputer dan Sistem Intelijen KOMMIT 2008, Depok, Indonesia.

Mala, V, Kusuma, A, Furqon, M. T., dan Muflikhah, L. (2017). Implementasi Metode Fuzzy Subtractive Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan / Lahan. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 1, no. 9, 876–884.

Mustaqhfiri, M, Abidin, Z, dan Kusumawati, R. (2012). Peringkasan Teks Otomatis Berita Berbahasa Indonesia Menggunakan Metode Maximum Marginal Relevance. Matics, vol. 4, no. 4.

Rokach, L. and Maimon, O. (2005) Clustering Methods. In O. Maimon and L. Rokach (Ed) Data Mining and Knowledge Discovery Handbook, Boston, MA: Springer US, 321–352.

Sastroasmoro, S. (2007). Beberapa Catatan tentang Plagiarisme. Majelis Kedokt. Indonesia., vol. Volum: 57, 239–244.

Wicaksono, Y.A. 2012. Analisis Dan Implementasi Algoritma Rabin-Karp Dan Algoritma Stemming Nazief-Adriani Pada Sistem Pendeteksi Plagiat Dokumen.

Winkler, W. E. (2006). Overview of Record Linkage and current research directions. Bureau of The Census., p. 44.

Title Deteksi Plagiarisme Skripsi Mahasiswa dengan Metode Single-link Clustering dan Jaro-Winkler Distance
Issue: Vol. 5 No. 1 (2020): JURNAL PILAR TEKNOLOGI
Section Articles
Published: Jun 9, 2020
  • Hidayat Abdul Rouf
  • Ardhi Wijayanto
  • Abdul Aziz