Naïve Bayes Classifier for Indoor Positioning using Bluetooth Low Energy

Abstract

Indoor localization becomes more popular along with the rapid growth of technology dan information system. The research has been conducted in many areas, especially in algorithm. Based on the need for knowledge of training data, Fingerprinting algorithm is categorized as the one that works with it. Training data is then computed with the machine learning approach, Naïve Bayes. Naïve Bayes is a simple and efficient classifier to estimate location. This study conducted an experiment with Naïve Bayes in order to classify unknown location of object based on the signal strength of Bluetooth low energy. It required 2 processes, collecting training data and evaluating test data. The result of the analysis with Naïve Bayes showed that the algorithm works well to estimate the right position of an object regarding its class.