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All Journal The Indonesian Journal of Computer Science
Akbar Ilahi
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Decision Support System for Determining the Best Online Learning Application Using the Weighted Product Method (WPM) Yeka Hendriyani; Mariani; Rahmatika, Hayati; Akbar Ilahi; Hardeyenti, Dessy
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3352

Abstract

This article discusses the use of the Weighted Product (WP) Method in a Decision Support System to determine the best online learning application. The goal is to help users choose a learning app that suits their needs and preferences. Relevant criteria, such as user interface, learning content, interactive features, difficulty level, and subscription price, are identified and assigned relative weights. This study uses data from various online learning apps to train the WP model and rank the apps based on the highest scores. The experimental results show that the WP method successfully identifies the best online learning apps with high accuracy, allowing users to have an effective and efficient learning experience according to their individual goals. Thus, this SDM can be an effective guide for users in making informed decisions to meet their education and learning needs
Co-Authors Hardeyenti, Dessy Mariani Rahmatika, Hayati Yeka Hendriyani