Knowledge Transfer About Earthquake Disaster Mitigation To Children Through TF-IDF

Maria Umran, Hafiz Mohd. Sarim


Abstract: Past observations during a disaster identify that when children are separated from parents, they suffer due to the inability to comprehend disaster mitigation concepts. This study proposes a process from the existing framework K-Nearest Neighbor (KNN) and Term Frequency - Inverse Document Frequency (TF-IDF) for extracting a large body of knowledge in the form of documents into simple words. Those simple words can be arranged into contextual lyrics utilizing an Artificial Intelligence lyrics generator and then orchestrated into a song using a music generator. The piece, which is the output of the proposed process, is utilized to transfer the knowledge about earthquake disaster mitigation to children. A quantitative analysis of questionnaires on students aged 9-10 in Banda Aceh shows the song's highly significant effect in transferring the knowledge about earthquake disaster mitigation to children.


TF-IDF; disaster mitigation; knowledge transfer

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Copyright (c) 2020 Maria Umran, Hafiz Mohd. Sarim

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P-ISSN : 2460-8912
E-ISSN : 2460-8920


Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Elkawnie: Journal of Islamic Science and Technology in 2022. Published by Faculty of Science and Technology in cooperation with Center for Research and Community Service (LP2M), UIN Ar-Raniry Banda Aceh, Aceh, Indonesia.

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