KLASIFIKASI DATA PASIEN COVID-19 MENGGUNAKAN ALGORITMA XGBOOST

ALREDHO, MUHAMMAD AGUNG and Rini, Dian Palupi and Saputra, Danny Matthew (2022) KLASIFIKASI DATA PASIEN COVID-19 MENGGUNAKAN ALGORITMA XGBOOST. Undergraduate thesis, Sriwijaya University.

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Abstract

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%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Machine Learning, COVID-19, XGBoost
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 25066 not found.
Date Deposited:	25 Nov 2022 02:47
Last Modified:	25 Nov 2022 02:47
URI:	http://repository.unsri.ac.id/id/eprint/82678

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