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Analisis Trafik Pengguna XL Axiata Jakarta Selama Pandemi Covid-19 dengan Menggunakan Metode Regresi Linier

Authors :
Theresiawati
Kraugusteeliana
Diana Dyah Damayanti
Source :
Journal of Applied Computer Science and Technology, Vol 2, Iss 1, Pp 24-32 (2021)
Publication Year :
2021
Publisher :
Indonesian Society of Applied Science (ISAS), 2021.

Abstract

2020 is a very disturbing year for the whole world because in 2020 there is a deadly virus, the virus is called Covid-19. The Indonesian government has a strategy to deal with the Covid-19 virus, namely by implementing Large-Scale Social Restrictions (PSBB). Where the implementation of this PSBB requires residents to do more activities at home. To carry out activities at home, internet access is needed. Therefore, providers are the companies that benefit the most, providers are companies that provide internet services, one of which is XL Axiata. During the Covid-19 pandemic, XL Axiata company wants to conduct analysis for XL Axiata in the next year, whether the traffic of XL Axiata users has increased or decreased and how many devices need to be upgraded or safe. In conducting this analysis, it is necessary to perform fast data processing, data processing using the Data Mining method with Linear Regression techniques. The data to be processed will be obtained from the XL Axiata company, namely XL Axiata traffic data. By processing data using Linear Regression results in future predictions, the West Jakarta Municipality has increased per week by an average of 0.12%, the East Jakarta Municipality has decreased per week by an average of 0.27%, the Central Jakarta Municipality experiences the decline per week by an average of 2.31%, the South Jakarta Municipality experienced an increase per week by an average of 1.17%, and in the North Jakarta Municipality it decreased per week by an average of 0.12%.

Details

ISSN :
27231453
Volume :
2
Database :
OpenAIRE
Journal :
Journal of Applied Computer Science and Technology
Accession number :
edsair.doi.dedup.....7c8a44a3c13b478b8735c6233f96181d
Full Text :
https://doi.org/10.52158/jacost.v2i1.158