Department of Informatics, Universitas Kristen Duta Wacana, Indonesia
BibTex Citation Data :
@article{JTSISKOM12932, author = {Yuan Lukito}, title = {Model Multi Layer Perceptron untuk Indoor Positioning System Berbasis Wi-Fi}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {5}, number = {3}, year = {2017}, keywords = {indoor positioning system;Wi-Fi positioning; multilayer perceptron}, abstract = { Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multilayer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multilayer perceptron implementation, training process of multilayer perceptron, evaluation, and analysis. The training process has been conducted many times to find the best parameters that produce the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures. }, issn = {2338-0403}, pages = {123--128} doi = {10.14710/jtsiskom.5.3.2017.123-128}, url = {https://jtsiskom.undip.ac.id/article/view/12932} }
Refworks Citation Data :
Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multilayer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multilayer perceptron implementation, training process of multilayer perceptron, evaluation, and analysis. The training process has been conducted many times to find the best parameters that produce the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures.
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