Indoor Positioning System in Learning Approach Experiments

Abstract

The positioning system research strongly supports the development of location-based services used by related business organizations. However, location-based services with user experience still have many obstacles to overcome, including how to maintain a high level of position accuracy. From the literature studies reviewed, it is necessary to develop an indoor positioning system using fingerprinting based on Received Signal Strength (RSS). So far, the testing of the indoor positioning system has been carried out with an algorithm. But, in this research, with the proposed parameters, we will conduct experiments with a learning approach. The data tested is the signal service data on the device in the Dinamika Bangsa University building. The test was conducted with a deep learning approach using a deep neural network (DNN) algorithm. The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. From the results of comparative testing with the learning approach between DNN, KNN, and SVM, it can be concluded that the evaluation with KNN is slightly better than the use of DNN in a single case. However, the results of KNN have low consistency; this is seen from the fluctuations in the movements of the R2 score and MSE values produced. Meanwhile, DNN gives a consistent value even though it has varied hidden layers. The Support Vector Machine (SVM) gives the worst value of these experiments, although, in the past, SVM was known as one of the favorite methods.