RANCANG BANGUN APLIKASI KURSUS ONLINE BERBASIS WEB DENGAN SISTEM REKOMENDASI METODE CONTENT-BASED FILTERING

Lembaga Kursus dan Pelatihan (LKP) merupakan pendidikan non-formal yang menyediakan berbagai pelatihan khusus untuk mendidik keterampilan pelajar. Namun, terdapat hal yang sering menjadi kendala bagi calon pelajar yaitu bingung melakukan dalam melakukan pemilihan kursus sesuai dengan preferensinya. Oleh karena itu, tujuan dari penelitian ini adalah merancang sebuah aplikasi kursus online dengan sistem rekomendasi metode Content-based Filtering. Selain itu, penelitian ini juga menerapkan algoritma cosine similarity untuk menentukan kursus yang serupa dengan riwayat kursus yang pernah diakses oleh pengguna. Dataset yang digunakan untuk menjalankan algoritma sistem rekomendasi diambil dari situs Kaggle berjudul “Udemy Course” dengan jumlah data sebanyak 3,682 records. Hasil akhir dari penelitian ini adalah sebuah aplikasi kursus online yang dapat memberikan rekomendasi kursus berdasarkan nilai kesamaan yang paling tinggi dari algoritma cosine similarity.The Institute for Courses and Training is a non-formal education that provides various special training to educate students' skills. However, there are things that often become obstacles for prospective students, namely being confused about choosing courses according to their preferences. Therefore, the purpose of this research is to design an online course application with a recommendation system for the Content-based Filtering method. In addition, this research also applies the cosine similarity algorithm to determine courses that are similar to the course history that has been accessed by the user. The dataset used to run the recommendation system algorithm was taken from the Kaggle website entitled “Udemy Course” with a total of 3,682 records. The final result of this research is an online course application that can provide course recommendations based on the highest similarity value from the cosine similarity algorithm

Content-based course recommender system for liberal arts education

Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we have developed for the Liberal Arts bachelor of the University College Maastricht, the Netherlands. It aims to complement academic advising and help students make better-informed course selections. The system recommends courses whose content best matches the student's academic interests, issues warnings for courses that are too advanced given the student's academic background and, in the latter case, suggests suitable preparatory courses. We base the course recommendations on a topic model fitted on course descriptions, and the warnings on a sparse predictive model for grade based on students' past academic performance and level of academic expertise. Preparatory courses consist of courses whose content has the best preparatory value according to the predictive model. We find that course recommendations are relevant for a wide range of academic interests present in the student population and that students found recommendations for courses at other departments especially helpful. The preparatory courses often lack coherence with the target course and need to be improved

Content-based course recommender system for liberal arts education

Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we have developed for the Liberal Arts bachelor of the University College Maastricht, the Netherlands. It aims to complement academic advising and help students make better-informed course selections. The system recommends courses whose content best matches the student's academic interests, issues warnings for courses that are too advanced given the student's academic background and, in the latter case, suggests suitable preparatory courses. We base the course recommendations on a topic model fitted on course descriptions, and the warnings on a sparse predictive model for grade based on students' past academic performance and level of academic expertise. Preparatory courses consist of courses whose content has the best preparatory value according to the predictive model. We find that course recommendations are relevant for a wide range of academic interests present in the student population and that students found recommendations for courses at other departments especially helpful. The preparatory courses often lack coherence with the target course and need to be improved