Nurzaqiah, Salma and Taufik, Ichsan and Maylawati, Dian Sa'adillah and Zulfikar, Wildan Budiawan and Dauni, Popon (2022) Implementation of K-Nearest Neighbor to Predict the Chances of COVID-19 Patients’ Recovery. In: 2022 8th International Conference on Wireless and Telematics (ICWT), 21-22 Juli 2022, Yogyakarta.
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Abstract
Coronavirus Disease 2019 (COVID-19) is a new disease discovered in 2019 in Wuhan, China, and then spread worldwide. Many victims have confirmed varying positive levels of infection based on the patient's immunity. This study aimed to predict the chances of COVID-19 patients' recovery based on the patient's symptoms and conditions. The method used is the K-Nearest Neighbor (KNN) algorithm. KNN produces two classes of predictions: the chance of recovering or the possibility of dying. Based on the experimental results on 496 data from patients who were confirmed positive for COVID-19, KNN predicted the chances of recovery for patients with confirmed COVID-19 with an average accuracy of 88.16%. A prediction system for the chance of recovery for COVID-19 patients is constructed by choosing the best model from five test scenarios based on the given k value. The best model is at a value of k equal to 4, with an accuracy value of 88.8%.
| Item Type: | Conference or Workshop Item (Paper) |
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| Subjects: | Technology, Applied Sciences |
| Divisions: | Fakultas Sains dan Teknologi > Program Studi Teknik Informatika |
| Depositing User: | Dian Sa'adillah Maylawati |
| Date Deposited: | 04 Apr 2023 02:04 |
| Last Modified: | 04 Apr 2023 02:04 |
| URI: | https://digilib.uinsgd.ac.id/id/eprint/66658 |
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