Indoor localization based Wi-Fi signal strength using support vector machine

Hani Rubiani, - and Sulidar Fitri, - and Muhammad Taufiq, - and Mujiarto, - (2019) Indoor localization based Wi-Fi signal strength using support vector machine. Journal of Physics: Conference Series, 1402. 077055. ISSN 17426588; 17426596

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Official URL: https://iopscience.iop.org/volume/1742-6596/1402v

Abstract

Ubiquitous computing to estimate the location of objects in a building raises a fundamental challenge and there has been a lot of research on localization in buildings based on signal strength by utilizing devices inside such as Wi-Fi signals. Positioning objects using algorithms of received signal strength in this paper using Linear Support Vector Machine which will be compared with Naïve Bayes. Experiments carried out using 14480 datasets and 302 classes were collected from the real world environment and the results showed that the system reached the correct classification level of around 88% and a minimum distance of error of 4.61 meters compared to Naïve Bayes for the correct classification level of around 67 % and average error distance of 6.21 meters.

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Item Type:	Article
Divisions:	Fakultas Teknik > Karya Dosen
Depositing User:	Tsani Karimah
Date Deposited:	01 Nov 2022 09:08
Last Modified:	19 May 2023 03:50
URI:	http://repository.umtas.ac.id/id/eprint/1110

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