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KLASIFIKASI DATA PASIEN COVID-19 MENGGUNAKAN ALGORITMA XGBOOST

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Penilaian

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Coronavirus Disease 19 or COVID-19 is a deadly virus that attacks the lungs and is very easy to transmit so that it has spread to all corners of the world. A system for the classification of COVID-19 patient data is needed so that patients can determine further treatment for COVID-19, such as self-isolation, or requesting advanced treatment in a hospital. XGBoost is an implementation of Gradient Boosted Decision Tree algorithm with several optimizations that can be used for both classification and regression problems. This algorithm uses a decision tree as a weak learner and gradient boosting as a framework. This study was conducted to determine the steps for classifying COVID-19 patient data with the XGBoost algorithm and to see how much accuracy can be obtained. The XGBoost model was trained on 135,682 data that has attributes such as gender, age, and the main symptoms of COVID-19. The study was conducted by dividing the dataset into March and April periods and using K-Fold Cross Validation with K values equal to 5 and 10. The results showed that the COVID-19 patient data classification model was successfully developed with an average accuracy of 94%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207004862	T82678	T826782022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T826782022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xviii, 116 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Pengolahan Data
Jurusan Teknik Informatika

Specific Detail Info

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Statement of Responsibility

SEPTA

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