Correlation Analysis of Multivariate Regression Algorithms on RSS Data for Indoor Positioning System
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Abstract
In this study, we used the Received Signal Strength (RSS) dataset that we were collecting at the University ofDinamikaBangsa Jambi Building. RSS dataset is data that can be utilized in the development of signal processing technology that is very useful in various fields. Our RSS dataset has dependent and independent variables. However, we need to know whether our dataset is feasible or not to be tested with Machine Learning. Therefore, testing is needed to determine the correlation value between the dependent and independent variables in our dataset. Some of the algorithms we use in testing the correlation values of this dataset are Partial least square (PLS), Canonical Correlation Analysis (CCA), and Partial Least Square Canonical (PLSC). From the test results obtained that the correlation of multiple variables in the dataset with the highest value of r2 score is PLS regression with a value of N = 3, R2_score of 0.630453 and MSE of 44.89262. The R2_score value obtained by PLS exceeds the target value of the correlation indicator with a good value of 0.6.
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