Model Multi Layer Perceptron untuk Indoor Positioning System Berbasis Wi-Fi

Copyright (c) 2017 Jurnal Teknologi dan Sistem Komputer

Article Metrics: (Click on the Metric tab below to see the detail)

Article Info
Submitted: 2017-05-31
Published: 2017-07-31
Section: Articles
Fulltext PDF Tell your colleagues Email the author

Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multi layer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multi layer perceptron implementation, training process of multi layer perceptron, evaluation and analysis. The training process has been conducted many times to find the best parameters that produces the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures.

Masalah penentuan posisi di dalam ruangan masih memerlukan banyak perbaikan. Penelitian ini mencoba melakukan eksplorasi terhadap penggunaan multi layer perceptron untuk penentuan posisi seseorang di dalam gedung atau ruangan, yang lebih dikenal dengan istilah Indoor Positioning System. Penelitian ini dilaksanakan dalam beberapa tahap yaitu normalisasi dataset, implementasi multi layer perceptron, pelatihan multi layer perceptron dan proses pengujian serta analisis. Proses pelatihan dilakukan beberapa kali untuk menemukan parameter-parameter yang menghasilkan akurasi terbaik. Dari hasil percobaan yang dilakukan didapatkan tingkat akurasi terbaik sebesar 79,16%. Hasil tersebut masih lebih rendah jika dibandingkan dengan hasil penelitian sebelumnya, sehingga memerlukan perubahan pengaturan parameter atau pengubahan arsitektur jaringan syaraf tiruan yang digunakan.


Posisi dalam ruangan; Wi-Fi; Multi layer perceptron


  1. A. Ozer and E. John, "Improving the Accuracy of Bluetooth Low Energy Indoor Positioning System Using Kalman Filtering," in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016.
  2. W. An, Z. Shen and J. Wang, "Compact Low-Profile Dual-Band Tag Antenna for Indoor Positioning Systems," IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 400-403, 2017.
  3. U. Miksaj and D. Bonefacic, "Propagation characteristics of UHF radiofrequency identification system signal for application in indoor positioning," in 2016 22nd International Conference on Applied Electromagnetics and Communications (ICECOM), 2016.
  4. Y. See, N. Noor and Calvin Tan Y.M, "Investigation of Indoor Positioning System using Visible Light Communication," in 2016 IEEE Region 10 Conference (TENCON), 2016.
  5. L. Chen, Chi-Ren Chen and Da-En Chen, "VIPS: A video-based indoor positioning system with centimeter-grade accuracy for the IoT," in 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017.
  6. A. Ismail, H. Kitagawa, R. Tasaki and K. Terashima, "WiFi RSS fingerprint database construction for mobile robot indoor positioning system," in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016.
  7. C. Ko and S. Wu, "A Proactive Indoor Positioning System in Randomly Deployed Dense WiFi Networks," in 2016 IEEE Global Communications Conference (GLOBECOM), 2016.
  8. L. Mainetti, L. Patrono and I. Sergi, “A Survey On Indoor Positioning System,” in 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2014.
  9. X. Chen and Z. Gao, “Indoor Ultrasonic Positioning System of Mobile Robot Based On TDOA Ranging and Improved Trilateral Algorithm,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 2017.
  10. Y. Lukito, and A. R. Chrismanto, “Perbandingan Metode-Metode Klasifikasi Untuk Indoor Positioning System,” Jurnal Teknologi Informasi dan Sistem Informasi, vol. 1, no. 2, pp. 123-131, 2015.
  11. M. Abadi et al., “TensorFlow: A System for Large Scale Machine,” in 12th USENIX Symposium on Operating System Design and Implementation (OSDI 16), 2016.

  1. Yuan Lukito  Orcid Scholar Sinta
    Program Studi Teknik Informatika, Universitas Kristen Duta Wacana, Indonesia


    Program Studi Teknik Informatika
    Fakultas Teknologi Informasi
    Universitas Kristen Duta Wacana