Perancangan Fitur Deteksi Kemiripan Dokumen Jawaban Tugas Mahasiswa pada Sistem Manajemen Pembelajaran dengan Metode K-Shingling dan Cosine Similarity
Abstract
Technology development can be used to support the teaching and learning process so it can runs more effectively and efficiently, and one of the applications that can be used to support this process is the Learning Management System (LMS). LMS provides an integrated platform starting from delivering learning materials to students, and evaluating learning process. The evaluation method that can be done at the LMS is giving assignments to students, and students can do submission by uploading their documents assignment answer. The common problem is many students do plagiarism by duplicate the answers of other students. To overcome these problems, this study will implement feature in Course Assignment Menu in LMS to detect the similarity of student assignment document using K-Shingling and Cosine Similarity methods. The K-Shingling algorithm is used to form shingles or word fragments based from all text in the task answer document that has gone through the pre processing stage, then each document have shingle value vector which will be used to compare the similarity values between documents using Cosine Similarity. The results obtained in this study is the developed feature is capable to detecting the similarity percentage of the assignment answer documents from each student, and from that percentage value it can indicate whether the students assignment answers is plagiarism to other students assignment answers.
Keyword : LMS; plagiarism; K-Shingling; Cosine Similarity
Downloads
References
[2] (2021) Universitas Medan Area. [Online]. Available: https://uma.ac.id/berita/apa-itu-learning-management-system-40lms41
[3] Fitriani, Yuni. “Analisa Pemanfaatan Learning Management System (LMS) Sebagai Media Pembelajaran Online Selama Pandemi COVID-19”. Journal of Information System, Informatics and Computing. Vol.4 No.2, Desember 2020.
[4] Putra, Miftakhul Ilmi S. Mutaqin, Imam. “Analisis Deteksi Plagiarisme Pada LMS (Learning Management Systems) Untuk Pembelajaran Online Calon Guru Madrasah Ibtidaiyah”. Jpdi: Jurnal Pendidikan Dasar Islam, Vol. 3, No. 1 : 01-15. April 2021.
[5] Simanullang, Irwan Saputra. “Perancangan Aplikasi Deteksi Kemiripan Dokumen Teks Menggunakan Algoritma Shingling”. Jurnal Sistem Komputer dan Informatika (JSON) Volume 2, Nomor 1. Hal: 36-41, Sep 2020.
[6] Sariwating, Andry Vegard. “Perancangan dan Implementasi Aplikasi Deteksi Kemiripan Citra Digital Menggunakan Algoritma Shingling dan Redundant Pixel Removal”. S. Kom. Artikel Ilmiah. Universitas Kristen Satya Wacana. 2016.
[7] Adiansyah, Muhamad Yusuf. “Perancangan dan Implementasi Aplikasi Deteksi Kemiripan Dokumen Menggunakan Algoritma Shingling dan MD5 Fingerprint”. S. Kom. Artikel Ilmiah. Universitas Kristen Satya Wacana, Des. 2014.
[8] Miller, Jahnae. “An Empirical Comparison of Code Similarity Algorithms”. NYUAD’s Capstone Project 2, Abu Dhabi, UAE. Spring 2020.
[9] Siswanto, Eric., Giap, Yo Ceng. “Implementasi Algoritma Rabin-Karp dan Cosine Similarity Untuk Pendeteksi Plagiarisme Pada Dokumen”. Jurnal Algor-Vol.1 No.2. 2020.
[10] Meilina, Lely., Kumara, I Nyoman Satya., Setiawan, Nyoman. “Literature Review Klasifikasi Data Menggunakan Metode Cosine Similarity dan Artificial Neural Network”. Majalah Ilmiah Teknologi Elektro, Vol. 20, No. 2, Juli – Desember 2021.
[11] Setyadi, I Wayan Adi., Khrisne, Duman Care., Suyadnya, I Made Arsa. “Automatic Text Summarization Menggunakan Metode Graph dan Ant Colony Optimization”. Majalah Ilmiah Teknik Elektro, Vol. 17, No. 1, Januari – April 2018.
[12] (2020) Universitas Raharja. [Online]. Available: https://raharja.ac.id/2020/04/05/metode-agile/
[13] Hardiyanti, Siti. “Perbandingan Distance Based Similarity Measure Pada Algoritma Rabin Karp Untuk Menghitung Kemiripan Teks”. S. Kom. Skripsi. Universitas Gadjah Mada. 2018.
[14] Li, Peng., Qiao, Tianling., Guang, Yongxing., Zhang, Lan. “A New Shingling Similar Text Detection Algorithm” in Proceedings of the Second International Symposium on Simulation and Process Modelling, 2020, paper, page 83.
[15] Varol, Cihan., Hari, Sairam. “Detecting Near-Duplicate Text Documents With A Hybrid Approach”. Journal of Information Science, April, 2015.
[16] Azgomi, Hossen., Mahsayeh, Masumeh Ghasemi., Mohammadic, Masoud., Rad, Milad Moradi. “A Method For Finding Similar Documents Relying on Adding Repetition of Symbols in Length Based Filtering”. Indian J.Sci.Res. 2(1) : 81-84, 2014.
[17] Mishra, Asha Rani., Panchal, V.K. “A Novel Approach to Capture the Similarity in Summarized Text Using Embedded Model”. International Journal on Smart Sensing and Intelligent Systems, Issue 1 Vol. 15, 2022
[18] Firdaus, Pasnur och Wabdillah. “Implementasi Cosine Similarity Untuk Peningkatan Akurasi Pengukuran Kesamaan Dokumen Pada Klasifikasi Dokumen Berita Dengan K Nearest Neighbour” Jurnal Teknologi Informasi dan Komunikasi , Vol. 1, Nomor 1, 2019.
[19] Manaa, Mehdi Ebady., Abdulameer, Ghufran. “Web Documents Similarity Using K-Shingle Tokens and MinHash Technique”. Journal of Engineering and Applied Sciences 13 (6): 1449-1505, 2018.
[20] Finansyah, Achmad Yohni Wahyu., Afiahayati, Sutanto, Vincent Michael. “Performance Comparison of Similarity Measure Algorithm as Data Preprocessing Stage: Text Normalization in Bahasa Indonesia”. Scientific Journal of Informatics, Vol. 9, No. 1, May 2022.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License