PREDIKSI SINYAL WI-FI MENGGUNAKAN KALMAN FILTER UNTUK SISTEM PENETUAN POSISI BERBASIS FINGERPRINT

AFRIANSYAH, REDHO and Stiawan, Deris and Ubaya, Huda (2021) PREDIKSI SINYAL WI-FI MENGGUNAKAN KALMAN FILTER UNTUK SISTEM PENETUAN POSISI BERBASIS FINGERPRINT. Undergraduate thesis, Sriwijaya University.

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Abstract

In recent years, the development of technology is growing rapidly, especially in the field of wireless technology, Along with this rapid development, the object positioning feature is also developing. The current Global Positioning System (GPS) feature has the disadvantage of not being able to accurately estimate the position when the object is inside the building, Wi-fi-based positioning with RSS technique is a better solution in estimating indoor positioning. In this study, the authors tested the RSS Fingerprint and Kalman Filter methods to estimate the position. The test was carried out in a building with 3 floors located at the Faculty of Computer Science, Sriwijaya University. There are 2 experimental scenarios, the first scenario is to test object positioning using the fingerprint method with the KNN classification without Kalman Filter getting the position results with the highest accuracy in space C1 is K1 = 1, K7 = 0.96, K15 = 0.95 and the lowest in space C3 is K1 = 0.16 , K7=0.12, K15=0.10. The second scenario is testing object positioning using the fingerprint method with KNN classification and the Kalman filter algorithm, result in space C1 is K1=1, K7=1, K15=1 and in space C3 is K1=0.63, K7=0.77, K15=0.78. From the results of this study, the RSS fingerprint method is able to estimate the position of the object and the Kalman filter algorithm can stabilize the data and increase position accuracy.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Penentuan Posisi, RSS, Fingerprint, Kalman Filter, Wifi, KNN
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television > TK5105.5.S72 Computer networks
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mr Redho Afriansyah
Date Deposited:	23 Sep 2021 02:07
Last Modified:	23 Sep 2021 02:07
URI:	http://repository.unsri.ac.id/id/eprint/54580

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