Perbandingan Metode-Metode Klasifikasi untuk Indoor Positioning System

Abstrak

Indoor Positioning System can provide position and navigation guidances inside a building.  This paper discusses about systematic comparison between K-Nearest Neigbors and Naïve Bayes Classifier over WiFi-based Indoor Position System dataset.  The dataset is collected using a custom Android Application, which able to receive and record WiFi signal strengths from the surrounding WiFi hotspots in UKDW campus. The dataset consists of 11658 Received Signal Strength (RSS) data from 41 public locations in UKDW campus.  We use 10-folds cross validation and T-Test with 0.05 significance level to compare classification accuracy between K-Nearest Neigbors and Naïve Bayes classifier.  Based on the experiment result, we can conclude that K-Nearest Neighbors classifier produces better classification accuracy (83.58%) than Naïve Bayes (61.52%).