Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Computer Networks, Architecture and High Performance Computing
Nazua, Sifania
Unknown Affiliation
Computer Science & IT Education

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparative Study of Iconnet Jabodetabek and Banten Using Linear Regression and Support Vector Regression Hasanudin, Muhaimin; Prihandi, Ifan; Nazua, Sifania
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3362

Abstract

PT PLN Indonesia Comnets Plus has little information regarding future customer growth, making it difficult to take steps to meet customer needs. This research aims to predict customer growth in the future based on quantitative data from the previous year, where the output provided produces data in the form of numbers that are analyzed using statistical methods. The hope is to provide information to maximize customer growth with the minimum area or bandwidth used by the company. This research uses linear regression and support vector regression (SVR) algorithms using a company secondary dataset of 252 data points with 5 attributes. Data was collected during the last one-year period, from January to December 2021. The results of the research show that predictions using both algorithms have increased, customer growth when viewed from the number of customers, bandwidth, and regional data has increased significantly. This can be seen from the value of the number of customers, which continues to increase, while the highest number of customers falls in December, the most requested bandwidth is 20 mbps, and the largest customer area is in the Depok area. The results of the research show that the SVR algorithm is superior in terms of mean absolute percentage error (MAPE): 0.02% MAPE, 0.10 MAE, and 0.99 RMSE, while for linear regression, the MAPE values were 36.28%, MAE 201, and RMSE 0.80.
Co-Authors Prihandi, Ifan