Naim Rochmawati, - and Hanik Badriyah Hidayati, - and Yuni Yamasari, - and Wiyli Yustanti, - and Lusia Rakhmawati, - and Hapsari P A Tjahyaningtijas, - and Yeni Anistyasari, - (2020) Covid Symptom Severity Using Decision Tree. In: 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE). IEEE, Surabaya, pp. 1-5.

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Abstract

Corona is a very contagious virus. In a pandemic like this, people often worry whether they are infected or not. When they cough, they often worry whether it is a sign of covid-19 or an ordinary cough. From the clinical symptoms can actually be known whether someone has Covid or not. In this study, a clinical symptom dataset will be used to classify the symptoms using a Decision Tree algorithm. The decision trees used in this research are J48 and Hoeffding Tree. Decision Tree is one of the most popular classification methods because it is easy to interpret by Humans. the prediction model uses a hierarchical structure. The concept is to convert data into decision trees or decision rules. the result of J48 were slightly better than the Hoeffding tree in terms of accuracy, precision, and recall. Meanwhile, from the tree view results, the Hoeffding Tree is simpler and the number of nodes is less than J48.

Item Type:	Book Section
Uncontrolled Keywords:	decision tree, corona, covid, covid-19, corona, symptoms, prediction, decision rules
Subjects:	R Medicine > R Medicine (General) R Medicine > RC Internal medicine
Divisions:	01. Fakultas Kedokteran > Ilmu Penyakit Saraf
Creators:	Creators NIM Naim Rochmawati, - UNSPECIFIED Hanik Badriyah Hidayati, - NIDN0024097808 Yuni Yamasari, - UNSPECIFIED Wiyli Yustanti, - UNSPECIFIED Lusia Rakhmawati, - UNSPECIFIED Hapsari P A Tjahyaningtijas, - UNSPECIFIED Yeni Anistyasari, - UNSPECIFIED
Depositing User:	arys fk
Date Deposited:	09 Apr 2021 03:30
Last Modified:	01 Sep 2022 07:21
URI:	http://repository.unair.ac.id/id/eprint/105306
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