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Klasterisasi Media Pembelajaran Daring Di Era Pandemi Covid-19 Menggunakan Metode Agglomerative


contributor author Ryan Ari Setyawan, Rizqi Mirza Fadilla
date accessioned Thu, 26 February 2026
date available Thu, 26 February 2026
date issued Kamis, 03 September 2020
description abstract This study aims to determine the use of learning media clusters from more effective and efficient in terms of user assessments, both students, and teachers in the midst of the Coronavirus Disease 2019 (Covid19) pandemic. The method used in this research is the agglomerative algorithm method hierarchial clustering to classify online learning media according to user ratings. This method will calculate the grouping or clustering with several agglomerative methods, namely the single linkage method, average linkage, and complete the environment. Each of these methods can perform distance calculations Euclidean distance, which will later determine the appropriate online learning media cluster user ratings. The data used is user assessment data (students, students and teachers). The result of clusterization for learning media that is most widely used is google classroom which is included in the learning application cluster, while for the internet, users choose internet data quota and for learning devices users use smartphones more.
subject Klasterisasi Media Pembelajaran Daring Di Era Pandemi Covid-19 Menggunakan Metode Agglomerative
title Klasterisasi Media Pembelajaran Daring Di Era Pandemi Covid-19 Menggunakan Metode Agglomerative
type

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