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All Journal Jurnal Teknik Informatika C.I.T. Medicom
Muhammad Nasir
STMIK LIKMI
Computer Science & IT

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Implementation of The Naïve Bayes Method in the COVID-19 Self-Assessment of Cianjur Regency Government Officials Muhammad Nasir; Andy Ramadhany Rahayu; I Putu Robin Sunjaya; Mey Sri Widialestari; Agus Prayitno
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 1 (2022): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol14.2022.239.pp16-26

Abstract

The impact of Covid-19 in Indonesia has penetrated into all fields of human activity including in the government sector, efforts to implement work from home for government agencies, especially in the Cianjur district to suppress the positive number of COVID-19 have been carried out. However, in practice the determination of employees to work from home is not appropriate, resulting in a decrease in the performance of government employees in Cianjur Regency, and an increase in positive numbers in the government environment. The method used in this research is an expert system approach with Naive Bayes which is the fastest and most accurate classification method for determining the problem. Based on the classification of the Naive Bayes method, samples were taken from Cianjur Regency government employees with symptoms of fever, cough, muscle aches, and loss of sense of smell, they had the highest probability of being classified as unhealthy and eligible for a swab test compared to other classifications, which was 80% percent. An expert system with a naive Bayes approach can be implemented to determine the health status of Cianjur Regency employees related to Covid-19, the suitability of the swab test, and the determination to work from home. For further research, it is suggested that it can be integrated with the existing institution's attendance system, and if necessary it can be tested with other methods.
Co-Authors Agus Prayitno Andy Ramadhany Rahayu I Putu Robin Sunjaya Mey Sri Widialestari