The Prediction of Recovery Rate of Covid 19 Case in Kabupaten Bandung Barat using Neural Network Algorithm - Tarihoran, Conrad Michael Kenneth; Hutapea, Lyna M. N. (8ISC Abstract Proceedings; 8ISC Abstract Proceedings, 2022)
The COVID-19 pandemic that happens worldwide has affected not only human health, social activities, the economy, education but also the death rate caused by this pandemic. Although the death rate from COVID-19 worldwide is quite high, the recovery rate is also quite promising; therefore, this study is conducted to predict the recovery rate of COVID-19 cases in Indonesia, which was analyzed using the Decision Tree C4.5 algorithm. The method of this study is data mining, using the Decision Tree C4.5 algorithm that analyzed data, consisting of 6 (six) Attributes and 1 class attribute, namely: Province, which represents the location in which the data was observed, Daily Case that represent the daily new confirmed case in the observed location, Daily Death that represents the daily new number of confirmed dead in observed location, Active Case that represents the daily new number of active case in observed location, Vaccinated that represent the total person who get Vaccinated ( 1st vaccination) and Fully Vaccinated that represent the total person who gets Full Vaccinated (2nd vaccination). The class attributes are using Daily Recovered, which represents the daily new number of confirmed recover in the observed location. The findings of this study indicate that the Decision Tree C4.5 Algorithm used in this study has an accuracy rate of 81.2% to predict the recovery rate of Covid 19 cases in the observed location. Keywords : Recovery Rate, Covid 19, C4.5 Algorithm