Performance Evaluation of RSSI Prediction Methods in Wireless Communication Networks
Abstract
Continuous communication services that ensure user connectivity with the communication network are important. The communication network should be able to accommodate erratic user movements with high mobility. This research studies the performance of three different RSSI prediction methods: decision trees, random forests, and linear regression. Evaluation is carried out using statistical metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and percentage accuracy. This analysis was carried out to understand how well each model predicted RSSI values based on distance. The research results show that Decision Tree Performance has an accuracy of 83.333%, Random Forest has a high accuracy of 97.2545%, and the Linear Regression Model provides quite good predictions with an accuracy of 91.6667% in predicting RSSI values.
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DOI: http://dx.doi.org/10.30829/zero.v8i1.19420
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Department of Mathematics
Faculty of Science and Technology
Universitas Islam Negeri Sumatera Utara Medan
Email: mtk.saintek@uinsu.ac.id