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Kinerja jaringan saraf berbasis backpropagation dan LVQ sebagai algoritme fingerprint RSS LoRa untuk penentuan posisi pada ruang terbuka

Neural network performance based on backpropagation and LVQ as the LoRa RSS fingerprint algorithms for positioning in an open space

Department of Electrical Engineering, Universitas Mataram, Indonesia

Received: 17 Nov 2019; Revised: 4 Feb 2020; Accepted: 10 Feb 2020; Available online: 15 Feb 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Outdoor positioning is one of the important applications in the Internet of things (IoT). The usage of GPS is unsuitable for low-power IoT devices. Alternatively, it can use the LoRa devices. This research aims to find a better method as the fingerprint algorithm for determining the outdoor position using RSS LoRa. The methods used as the fingerprint algorithm were two artificial neural network models, i.e. backpropagation (BP) with four types of training methods and learning vector quantization (LVQ) with two types of training methods. The experiment results show the performance of LVQ1 better than those of LVQ2. Besides, the LVQ1 was also better than the BP method. However, both BP and LVQ2 have a performance that is almost similar to about 70 %. Both of the artificial neural network models, BP and LVQ, can be used as a fingerprint algorithm to determine quite accurate the outdoor object position.
Keywords: neural network; backpropagation; learning vector quantization; fingerprint algorithm; RSS LoRa
Funding: LPPM Universitas Mataram under contract 2514/UN18.L1/PP/2019

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