Machine Learning Untuk Estimasi Posisi Objek Berbasis RSS Fingerprint Menggunakan IEEE 802.11g Pada Lantai 3 Gedung JTETI UGM

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Published May 10, 2015
DOI https://doi.org/10.20895/infotel.v7i1.23

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Chairani Chairani
Jurusan Teknik Informatika IBI Darmajaya
Widyawan Widyawan
Jurusan Teknik Elektro dan Teknologi Informasi Universitas Gadjah Mada
Sri Suning Kusumawardani
Jurusan Teknik Elektro dan Teknologi Informasi Universitas Gadjah Mada

Abstract

Penelitian ini membahas tentang estimasi posisi (localization) objek dalam gedung menggunakan jaringan wireless atau IEEE 802.11g dengan pendekatan Machine Learning. Metode pada pengukuran RSS menggunakan RSS-based fingerprint.  Algoritma Machine Learning yang digunakan dalam memperkirakan lokasi dari pengukuran RSS-based menggunakan Naive Bayes.  Localization dilakukan pada lantai 3 gedung Jurusan Teknik Elektro dan Teknologi Informasi (JTETI) dengan luas 1969,68 m2 dan memiliki 5 buah titik penempatan access point (AP). Untuk membentuk peta fingerprint digunakan dimensi 1 m x 1 m sehingga terbentuk grid sebanyak  1893 buah. Dengan menggunakan software Net Surveyor terkumpul data kekuatan sinyal yang diterima (RSS) dari jaringan wireless ke perangkat penerima (laptop) sebanyak 86.980 record. Hasil nilai rata-rata error jarak estimasi untuk localization seluruh ruangan di lantai 3 dengan menggunakan algoritma Naive Bayes pada fase offline tahap learning adalah 6,29 meter. Untuk fase online dan tahap post learning diperoleh rata-rata error jarak estimasi sebesar 7,82 meter.

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How to Cite
[1]
C. Chairani, W. Widyawan, and S. S. Kusumawardani, “Machine Learning Untuk Estimasi Posisi Objek Berbasis RSS Fingerprint Menggunakan IEEE 802.11g Pada Lantai 3 Gedung JTETI UGM”, INFOTEL, vol. 7, no. 1, pp. 1-8, May 2015.
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Vol 7 No 1 (2015): May 2015
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