Text Preprocessing Approaches in CNN for Disaster Reports Dataset

Arisha, Andriansyah Oktafiandi and Hazriani, Hazriani and Yuyun, Yuyun (2023) Text Preprocessing Approaches in CNN for Disaster Reports Dataset. Proceeding of 2023 International Conference on Artificial Intelligence in Information and Communication. pp. 216-2020. ISSN 2831-6983

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Abstract

This study aims to compare the performance of the text-preprocessing methods namely automatic and semi-automatic preprocessing techniques in the CNN algorithm to carry out learning on disaster report dataset. The experimental results on the disaster dataset with a total of 200 records with the automatic text preprocessing technique produce an average accuracy of 0.81 and 1 with training data of 80:20 and 90:10. While in the optimize model that is semi-automatic text preprocessing approach (which is the author's proposed approach), the average accuracy obtained are 0.95 and 1 for dataset 80:20 and 90:10. The experimental results conclude that cleaning the dataset with the semi-automatic text preprocessing model can improve accuracy compared to the previous model. The proposed model will get convergence with 80:20 training data at epoch 20, batch size 5 and random state 34, while for dataset 90:10 the best convergence value at epoch 20-30.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User:	Dr. Eng. Hazriani S.Kom., M.T.
Date Deposited:	24 Sep 2022 02:45
Last Modified:	24 Sep 2022 02:45
URI:	http://repo.handayani.ac.id/id/eprint/152

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