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Musriatun Napiah
Universitas Bina Sarana Informatika
Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science

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Journal : Computer Science (CO-SCIENCE)

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Komparasi Algoritma Machine Learning untuk Klasifikasi Gejala Coronavirus Disease 19 (Covid-19) Musriatun Napiah; Rachmawati Darma Astuti; Eka Kusuma Pratama
Computer Science (CO-SCIENCE) Vol. 3 No. 2 (2023): Juli 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v3i2.1984

Abstract

COVID-19 or Corona Virus Disease 19 is a member of the extended family of coronaviruses that cause a spectrum of illnesses from mild to severe, including MERS and SARS. While the cause of COVID-19 transmission has not been confirmed, it is believed that the virus is transmitted from animals to humans, causing various symptoms such as cough, runny nose, fever, sore throat and loss of smell. Research was conducted to classify COVID-19 symptoms into low, medium, and high categories in patients. This study aims to classify patient data and determine the risk of COVID-19 infection based on the severity of symptoms, namely mild, moderate, and high. Machine learning methods, including Decision Tree and SVM algorithms, are introduced and compared with K-Nearest Neighbor (K-NN), Neural Network (NN), Random Forest (RF), and Naive Bayes. The dataset used contains 127 patient records from kaggle.com. The test results showed that SVM achieved 54% accuracy, while Decision Tree achieved 98%. This research provides important insights into the risk assessment of COVID-19 infection based on symptom severity, and the use of machine learning techniques is expected to improve analysis and prediction capabilities in the face of the COVID-19 pandemic.
Co-Authors Eka Kusuma Pratama Ilham Nur Leksono Jordy Lasmana Putra Jordy Lasmana Putra Mugi Raharjo Mugi Raharjo Mugi Raharjo Mustofa Mustofa Rachmat Adi Purnama Rachmawati Darma Astuti Sujiliani Heristian Tommi Alfian Armawan Sandi Waeisul Bismi