Indoor Positioning System dengan Algoritma K-Means dan KNN

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Hizkia Juan Suryanto
Antonius Rachmat C.
Yuan Lukito


Indoor Positioning System (IPS) can determine someone’s position inside a building. The common method used is implemented by WiFi signal strength analising. This paper discusses about how to do IPS using K-Means and K-Nearest Neighbor (KNN) method, that also analyze the accuracy. K-Means is used to cluster dataset. Each data in certain cluster then classified using KNN method. The dataset consists of 11658 Received Signal Strength (RSS) from 177 Access Point (AP) in UKDW. Accuracy of system analized using 10-fold Cross Validation method which is applied in a range of k=2 to k=11 for clusterisation process, then k=1 to k=5 for classification process. Based on the experiment results, system can determine someone’s position with 88.49% accuracy which k optimum is 10 for clusterisation process, and k=1 for classification process.


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How to Cite
H. J. Suryanto, A. R. C., and Y. Lukito, “Indoor Positioning System dengan Algoritma K-Means dan KNN”, JuTISI, vol. 2, no. 3, Dec. 2016.