Penggunaan Algoritma Backpropagation dalam penentuan Lokasi Objek menggunakan Kekuatan Sinyal Wifi

Hani Rubiani

doi:10.24036/jtev.v5i1.1.106145

Penggunaan Algoritma Backpropagation dalam penentuan Lokasi Objek menggunakan Kekuatan Sinyal Wifi

Hani Rubiani⁽¹⁾,
(1) Universitas Muhammadiyah Tasikmalaya

Indonesia

Corresponding Author

DOI : https://doi.org/10.24036/jtev.v5i1.1.106145

Full Text:

Language : en

Abstract

This research was conducted to estimate the location of objects in buildings which are fundamental challenges and indeed there have been many studies on localization in buildings based on signal strength by utilizing devices in it such as Wifi signals. The position of the object to receive signal strength calculated in this paper uses a classification algorithm called Artificial Neural Networks (ANN) or in English known as Artificial Neural Network (ANN) with a type of feed forward network or Backpropagation. The experiments conducted showed that the achievement of the accuracy value of the validation results based on the dataset was around 44.56%.

Keywords

WiFi signal, Artificial Neural Network (ANN), Backpropagation, Artificial Neural Network, Object Location.

References

Q. Yang, S. J. Pan, and V. Wenchen Zheng, “Estimating Location Using Wi-Fi”,IEEE Intelligent Systems,” IEEE Intelligent Systems, vol. 23, no. 1, pp. 8–13, 2008.

H. Rubiani, “PENENTUAN POSISI OBJEK DI DALAM GEDUNG BERDASARKAN GSM MENGGUNAKAN METODE SUPPORT VECTOR MACHINE,” 1, vol. 3, no. 1, pp. 39–46, Apr. 2019.

H. Rubiani and M. Taufiq, “RSS FINGERPRINT MENGGUNAKAN SENSOR FUSION UNTUK ESTIMASI LOKASI DI DALAM GEDUNG,” 1, vol. 2, pp. 19–25, Apr. 2018.

A. Sudarsono, “JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI LAJU PERTUMBUHAN PENDUDUK MENGGUNAKAN METODE BACPROPAGATION (STUDI KASUS DI KOTA BENGKULU),” vol. 12, no. 1, p. 9, 2016.

T. Liu, S. Fang, Y. Zhao, P. Wang, and J. Zhang, “Implementation of Training Convolutional Neural Networks,” p. 10.

L. Zajmi, F. Y. H. Ahmed, and A. A. Jaharadak, “Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks,” Applied Computational Intelligence and Soft Computing, vol. 2018, pp. 1–7, Sep. 2018.

B. Lin et al., “Application of back-propagation artificial neural network and curve estimation in pharmacokinetics of losartan in rabbit,” Int J Clin Exp Med, vol. 8, no. 12, pp. 22352–22358, Dec. 2015.

M. M. Raju, R. K. Srivastava, D. C. S. Bisht, H. C. Sharma, and A. Kumar, “Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge,” Advances in Artificial Intelligence, vol. 2011, pp. 1–11, 2011.