Rancang Bangun Sistem Deteksi Posisi Objek dalam Rumah dengan Metode Support Vector Machine Berdasar Kekuatan Sinyal Wi-Fi


Damar Buana Murti(1*), Danang Lelono(2), Roghib Muhammad Hujja(3)

(1) Program Studi Elektronika dan Instrumentasi, DIKE, FMIPA, UGM, Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author


 Indoor Positioning System (IPS) is an object tracking technology that utilizes networks such as Wireless Fidelity (Wi-Fi) to determine the location of an object. IPS is closely related to the implementation of the Internet of Things (IoT) to carry out an order in a smart home. However, the weakness of IPS is the attenuation of the signal received when the tag or target moves to a room that borders another room, causing errors in tracking. The IPS implementation will be carried out based on the 2.4 GHz Wi-Fi signal emitted from the ESP32.

The research will use the trilateration method which requires three sink nodes to receive signal strength, then a machine learning algorithm, namely Support Vector Machine (SVM), to classify rooms in three different scenarios, namely when the target is stationary, moving between rooms, and is on the edge room adjacent to another room.

The results of the test show that the three scenarios provide different levels of accuracy. The accuracy of the system on the target scenario while still in the room reaches 100%, on the target moving room scenario reaches 86.15%, and on the target scenario that is at the edge of the room adjacent to another room reaches 80%.


Indoor positioning system; trilateration; SVM; Wi-Fi

Full Text:



[1] G. M. B. Oliveira et al., “Comparison between MQTT and WebSocket Protocols for IoT Applications Using ESP8266,” in 2018 Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2018 - Proceedings, Aug. 2018, pp. 236–241. doi: 10.1109/METROI4.2018.8428348.

[2] K. Mekki, E. Bajic, and Meyer Fernand, “Indoor Positioning System for IoT Device based on BLE Technology and MQTT Protocol,” in 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019, pp. 787–792. doi: 10.1109/WF-IoT.2019.8767287.

[3] A. Sashida, Di. P. Moussa, M. Nakamura, and H. Kinjo, “A Machine Learning Approach to Indoor Positioning for Mobile Targets using BLE Signals,” 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019, Jun. 2019, doi: 10.1109/ITC-CSCC.2019.8793423.

[4] P. Sthapit, H.-S. Gang, and J.-Y. Pyun, “Bluetooth Based Indoor Positioning Using Machine Learning Algorithms,” in 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia), 2018, pp. 206–212. doi: 10.1109/ICCE-ASIA.2018.8552138.

[5] H. A. Al-Jamimi and A. Al-Roubaiey, “Hybrid Modelling Based on SVM and GA for Intelligent Wi-Fi-based Indoor Localization System,” in 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2019, pp. 1–6. doi: 10.1109/ECAI46879.2019.9042102.

[6] H. A. Abbas, N. W. Boskany, K. Z. Ghafoor, and D. B. Rawat, “Wi-Fi Based Accurate Indoor Localization System using SVM and LSTM Algorithms,” in Proceedings - 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science, IRI 2021, 2021, pp. 416–422. doi: 10.1109/IRI51335.2021.00065.

[7] Shuai Zhang, Jiming Guo, Wei Wang, and Jiyuan Hu, “Indoor 2.5D Positioning of WiFi Based on SVM,” in 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), 2018, pp. 1–7. doi: 10.1109/UPINLBS.2018.8559903.

[8] D. Eridani and E. D. Widianto, “Performance of Sensors Monitoring System using Raspberry Pi through MQTT Protocol,” in 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2018, pp. 587–590. doi: 10.1109/ISRITI.2018.8864473.

[9] O. Barybin, E. Zaitseva, and V. Brazhnyi, “Testing the Security ESP32 Internet of Things Devices,” 2019 IEEE International Scientific-Practical Conference: Problems of Infocommunications Science and Technology, PIC S and T 2019 - Proceedings, pp. 143–146, Oct. 2019, doi: 10.1109/PICST47496.2019.9061269.

[10] A. Kowalczyk, Support Vector Machines Succinctly. Morrisville, North Carolina: Syncfusion, 2017. Accessed: Apr. 13, 2022. [Online]. Available: https://www.syncfusion.com/succinctly-free-ebooks/support-vector-machines-succinctly

DOI: https://doi.org/10.22146/ijeis.80736

Article Metrics

Abstract views : 284 | views : 266


  • There are currently no refbacks.

Copyright (c) 2023 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijeis.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijeis

View My Stats1
View My Stats2