Multicriteria Feature to Predict Final Score Using a Recommendation System

Dina Fitria Murad, Bina Nusantara University
Meta Amalya Dewi, Bina Nusantara University
Arbaiah Inn, Universiti Teknologi Malaysia  
Silvia Ayunda Murad, Bina Nusantara University
Noor Udin, Bina Nusantara University
Taufik Darwis, Bina Nusantara University

https://doi.org/10.9743/JEO.2025.22.1.20

Abstract

This study aims to produce a more personalized recommendation system for online learning using multicriteria in collaborative filtering and data from the Binus Online Learning repository as a knowledge base. The study uses forecasting (regression) and consists of three stages: (1) collecting data on the results of the learning process; (2) adding context data; and (3) providing recommendations based on decisions generated from a rule. Recommendations are made based on mapping the distribution of online learning material topics and the assessment rubrics contained in the course outline in each course. This study uses a comparative evaluation of several measurement errors using six regression models, namely (a) a generalized linear model, (b) deep learning, (c) a decision tree, (d) random forests, (e) gradient-boosted trees, and (f) a support vector machine. The results show that the most suitable model that provides the best prediction is the gradient-boosted trees model. Prediction results on student grades are used to provide recommendations that contain links to material that can be learned by students.

Keywords: multicriteria feature, recommendation system, link material, collaborative filtering.