WLAN FINGERPRINT UNTUK PREDIKSI LOKASI OBJEK DALAM GEDUNG
Keywords:
RSS, k-NN, Naïve Bayes, FingerprintAbstract
Kemajuan teknologi Wireles LAN sudah sangat banyak digunakan publik sebagai media komunikasi dan banyak diaplikasikan di berbagai tempat, mulai dari kampus, pertokoan, perkantoran bahkan tempat-tempat umum. Teknologi wireless LAN dengan memanfaatkan nilai RSS (Received Signal Strength) yang diperoleh dari acces point (AP) yang sudah ada bisa di aplikasikan untuk estimasi lokasi objek dalam ruangan . latar belakang Hal ini yang mendasari untuk estimasi lokasi objek dalam ruangan dengan metode fingerprint.
Penelitian ini difokuskan pada pemanfaatan RSS mengunakan 5 acces point dan lokasi penelitian dilakukan di lantai 3 gedung JTETI UGM. Pengambilan data fingerprint dilakukan dengan grid 1 x 1 meter bertujuan untuk mendapatkan tingkat akurasi yang tinggi. Memprediksi lokasi objek ini dengan metode fingerprint mengunakan algoritma k-Nearest Neighbor (kNN) dan Naïve Bayes.
Dari fase off-line hasil Visualisasi pada peta fingerprint menunjukan bahwa kekuatan sinyal yang diterima disetiap grid pengukuran berbeda. fase on-line rata-rata kesalahan jarak terhadap estimasi lokasi algoritma Naïve Bayes lebih baik dibanding dari algoritma kNN.
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