<![CDATA[Positioning is a technique used to determine the position of an object. There are two types of positioning technique: outdoor positioning and indoor positioning. An example of a system that can be used to measure outdoor positions is the Global Positioning System (GPS). GPS is a very common technology that known to know a position and as a pointer for displacement of objects through signals from satellites. GPS can provide good positioning in an outdoor environment, but the signal is very weak when used in a closed / indoor environment. Accordingly then developed a technology that serves to know the position of an object in indoor called Indoor Positioning. In this research we implements Indoor Positioning with Fingerprint method (signal strength recognition method) using measurement of signal strength (Received Signal Strength/RSS), that is by analyzed patterns strength of signal access point coming to receiver from every room. The first thing we did is to collect training data first as a basis for classification, then give the label. Next, we create a classifier based on training data. After that we re-measure as data testing to test its accuracy by Fuzzy K-Nearest Neighbor (FK-NN) classification method, and to make it easier to access the classifier that has been made, we use web service. The result of client position gives an accuracy level on K-Nearest Neighbor (K-NN) method with value k = 1 has value reaches 96%, for k=2 to k=7 has value reach 76%, and for k=8 to k=10 has value reach 73%. Meanwhile, FK-NN method with value k=1 and k=2 has value reach 96%, for k=3 to k=8 has value reach 76%, for k=9 has value reach 73%, and for k=10 has value reach 76%. Therefore, the implementation of Fuzzy K-Nearest Neighbor (FK-NN) classification method for Fingerprint Access point on Indoor Positioning has an enough accuracy level than the K-NN method.]]>
