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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Applied Computer Science and Technology (JACOST)
Alvina Felicia Watratan
STMIK Profesional Makassar
Computer Science & IT Engineering

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Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia Alvina Felicia Watratan; Arwini Puspita. B; Dikwan Moeis
Journal of Applied Computer Science and Technology Vol 1 No 1 (2020): Juni 2020
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v1i1.9

Abstract

The COVID-19 pandemic is the first and foremost health crisis in the world. Coronavirus is a collection of viruses from the subfamily Orthocronavirinae in the Coronaviridae family and the order of Nidovirales. This group of viruses that can cause disease in birds and mammals, including humans. In humans, coronaviruses cause generally mild respiratory infections, such as colds, although some forms of disease such as; SARS, MERS, and COVID-19 are more deadly. Anticipating and reducing the number of corona virus sufferers in Indonesia has been carried out in all regions. Among them by providing policies to limit activities out of the house, school activities laid off, work from home (work from home), even worship activities were laid off. This has become a government policy based on considerations that have been analyzed to the maximum, of course. Therefore this research was carried out as an anticipation step towards the Covid-19 pandemic by predicting the spread of Covid-19, especially in Indonesia. The research method applied in this research is problem analysis and literature study, collecting data and implementation. The application of the naive bayes method is expected to be able to predict the spread rate of COVID-19 in Indonesia. The results of the Naive Bayes method classification show that 16 data from 33 data were tested in Covid-19 cases per province with an accuracy of 48.4848%, where of the 33 data tested in the Covid-19 case per province tested there were 16 data that were successfully classified correctly.
Comparison of Naive Bayes and PSO-Based Naive Bayes Algorithms for Prediction of Covid-19 Patient Recovery Data in Indonesia Alvina Felicia Watratan; Ema Utami; Anggit Dwi Hartanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4893

Abstract

A brand new disease known as COVID 19 was identified in 2019 but has yet to infect humans (World Health Organization, 2019). This group of viruses can infect mammals, including humans and birds, and cause sickness. People commonly contract coronaviruses from the flu and other minor respiratory diseases, but they can also spread serious diseases such as SARS, MERS, and the deadly COVID-19. Therefore, to avoid further casualties, this number must be decreased. It is crucial to understand the variables that can truly reduce the danger of death and gauge the propensity for recovery in Covid-19 patients. Several techniques in data mining can be used to forecast patient recovery rates depending on various characteristics. The criteria of this study included gender, age, province, and status. The Naive Bayes (NB) and Pso-based Naive Bayes algorithms are compared in this study using patient data sets to determine whether the strategy is more accurate. The findings of this study reveal that the NB method has a 94.07% accuracy rate, a precision value of 14%, a recall value of 1% and an AUC value of 0.613, according to the study data. The accuracy rate of the Naive Bayes based on PSO is 95.56%, the precision is 25%, the recall is 1%, and the AUC is 0.540.
Co-Authors Anggit Dwi Hartanto Arwini Puspita. B Dikwan Moeis Ema Utami