Abstract
E-learning is an online learning system that applies information technology in the teaching process. E-learning used to facilitate information delivery, learning materials and online test or assignments. The online test in evaluating students’ abilities can be multiple choice or essay. Online test with essay answers is considered the most appropriate method for assessing the results of complex learning activities. However, there are some challenges in evaluating students essay answers. One of the challenges is how to make sure the answers given by students are not the same as other students answers or 'copy-paste'. This study makes a similarity detection system (Similarity Checking) for students' essay answers that are automatically embedded in the e-learning system to prevent plagiarism between students. In this paper, we use Artificial Neural Network (ANN), Latent Semantic Index (LSI), and Jaccard methods to calculate the percentage of similarity between students’ essays. The essay text is converted into array that represents the frequency of words that have been preprocessed data. In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value. The experimental results show that the ANN method in detecting text similarity has closer performance to the Jaccard method than the LSI method and this shows that the ANN method has the potential to be developed in further research.
Highlights
E-learning is an online learning system that applies information technology in the teaching process
In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value
A. Gozali, “Improving Text Preprocessing for Student ratio of crude oil systems,” Petroleum, vol 6, no
Summary
Perkembangan pesat teknologi mempengaruhi semua bidang kehidupan manusia. Teknologi membantu kehidupan manusia dalam menyelesaikan pekerjaan dengan cepat, efisien dan menekan human error. Salah satu teknologi informasi Deteksi kesamaan pada IR secara teknis memiliki pembelajaran jarak jauh adalah dengan menggunakan beberapa langkah sebelum masuk ke metode utama. Tugas deteksi tingkat kesamaan jawaban antar siswa gambaran bahwa teknik pendeteksian kesamaan teks secara teknologi bisa dimasukkan sebagai fitur dalam e- telah banyak dilakukan dengan berbagai macam metode learning dengan menggunakan teknologi Natural mulai dari metode algoritma matematis seperti Ratcliff, Language Processing (NLP) pada sub bidang ilmu Jaccard, Cosine Similarity, Winnowing dan LSI, sampai. Akses peran ke informasi [6], sehingga pengajar Deteksi kesamaan teks menggunakan neural network dimudahkan dalam melihat tingkat kesamaan jawaban memang mulai telah banyak dikerjakan namun khusus essay antar siswa untuk neural network dengan arsitektur AND Operator belum pernah diteliti secara langsung untuk kasus text.
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