Expert System of Text Mining to Analyze Student Interaction in FTKI UNAS Online Lectures Expert System of Text Mining to Analyze Student Interaction in FTKI UNAS Online Lectures
Article Sidebar
Main Article Content
Abstract
Interactivity is the student activities or relationships between students with anything related to the course, his example by attending a class attendance responds directly at the appointed hour. What is meant here is the liveliness activities interactivity of students to answer the questions that exist in the online lecture forum, whether he is copying and pasting answers with other students or not. The number of student answers archives make web manager can not judge whether the student was copying and pasting the answer to her or not. Therefore we need a system that can provide a connection weights to archives word answers and calculates similarities between students answer word. To analyze the data from these students, writers manipulate the data using text mining. This research using tf-idf method cosine similarity, the parameters used are folding case, tokenizing, stopwords and stemming. The results of this study are that have a common answer to answer other students not considered effective in the online lectures, corresponding similarity weight percentage of statutes that have been determined.
Downloads
Article Details
[2] Jian-Hong Jiang, Rui-Yun MA “A Code Classification Method Based On TF-IDF” Guilin University of Electronic Technology, ISBN: 978-1-60595-552-0, 2018.
[3] Muhammad Andi Al-Rizki, Galih Wasis Wicaksono, Yufis Azhar “The Analysis Of Proximity Between Subjects Based On Primary Contents Using Cosine Similarity On Lective” Universitas Muhammadiyah Malang, Vol. 2, No. 4, November 2017.
[4] Mali Fauzi, Djoko Cahyo Utomo, Budi Darma Setiawan, Eko Sakti Pramukantoro. “Automatic Essay Scoring System Using N-Gram And Cosine Similarity For Gamification Based Elearning” Universitas Brawijaya, Agustus 2017.
[5] Syed Hafeez, Balkrishna Patil “Using explicit semantic similarity for an improved web explorer with ontology and TF-IDF” Everest Education Society’s College of Engineering and technology, Aurangabad, India, Vol 2, issue 7, Jan 2017
[6] Maedeh Afzali, and Suresh Kumar “Comparative Analysis Of Various Similarity Measure For Finding Similarity Of Two Documents” Manav Rachna International University, india. Vol.10 No.2 (2017).
[7] Syahroni Wahyu Iriananda, Muhammad Aziz Muslim, Harry Soekotjo Dachlan “Identifikasi Kemiripan Teks Menggunakan Class Indexing Based dan Cosine Similarity Untuk Klasifikasi Dokumen Pengadua” Volume 10, No. 2 (2018).
[8] Rizki Tri Wahyuni, Dhidik Prastiyanto, Eko Suprapto “Penerapan Algoritma Cosine Similarity dan Pembobotan TF-IDF pada sistem Klasifikasi Dokumen Skripsi” Universitas Negeri Semarang, Januari-Juni 2017 Vol. 9 No.1.
[9] Rhevitta Widyaning Palupi, Yuita Arum Sari, Putra Pandu Adikara. “Prediksi Rating Novel Baru Berdasarkan Sinopsis Menggunakan Genre Based Collaborative Filtering dan Text Similarity” Universitas Brawijaya, Vol. 3, No. 3, Maret 2019
[10] Rito Putriwana Pratama, Muhammad Faisal, Ajib Hanani, “Deteksi Plagiarisme pada Artikel Jurnal Menggunakan Metode Cosine Similarity” Universitas negeri islam maulana malik ibrahim Malang, Vol.5 No. 1 2019.
[11] Ria Melita, Victor Amrizal, Hendra Bayu Suseno, Taslimun Dirjan. “Penerapan Metode Term Frequency Inverse Document Frequency (tf-idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadist Berbasis Web (Studi Kasus : Syarah Umdatil Ahkam” Universitas Islam Negeri Syarif Hidayatullah Jakarta, VOL 11 NO. 2, Oktober 2018
[12] Ni Luh Ratniasih, Made Sudarma, Nyoman Gunantara. “Penerapan Text Mining dalam Spam Filtering untuk Aplikasi Chat” Teknologi Elektro, Vol. 16, No. 3,September - Desember 2017.
[13] Fauzi Bayu Sejati, Purwono Hendradi, Bambang Pujiarto. “Deteksi Plagiarisme Karya Ilmiah Dengan Pemanfaatan Daftar Pustaka Dalam Pencarian Kemiripan Tema Menggunakan Metode Cosine Similarity” Universitas muhammadiyah magelang, Vol. 2 No. 2 | Januari 2019.
[14] Hartanto “Text mining dan sentimen analisis twitter pada gerakan LGBT”
[15] Ervita Kusuma Putri, Tedy Setiadi “Penerapan text mining pada sistem klasifikasi email spam menggunakan naive bayes”