Indoor Positioning System dengan Algoritma K-Means dan KNN

Abstract

Indoor Positioning System (IPS) can determine someone's position inside a building. The common method used is implemented by WiFi signal strength analysing. 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 is analyzed using 10-fold Cross Validation method which is applied in a range of k=2 to k=11 for clusterization 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 clusterization process, and k=1 for classification process.

Diabetes Classification Analysis Using the Euclidean Distance Method Based on the K-Nearest Neighbors Algorithm

“…Banyaknya atribut dapat mempengaruhi performa suatu algoritma [6]. Masalah klasifikasi pada dasarnya adalah sebagai berikut [7] Beberapa algoritma dapatdigunakan untuk melakukan tugas klasifikasi [8]. Salah satu algoritma klasifikasi data mining yang terbaik adalah K-Nearest Neighbour (KNN) [9].…”