Implementation K-Nearest Neighbor Algorithm in Searching Location Books in Library Statically Based on RFID
- DOI
- 10.2991/aisr.k.200424.045How to use a DOI?
- Keywords
- identification, K-Nearest Neighbor (KNN) algorithm, RFID
- Abstract
Radio Frequency Identification (RFID) is a technology for determining an object using electromagnetic waves (radio waves) through a device called a tag. This identification is carried out using a reader, tag, and antenna. To maximize the performance of RFID technology in a room with a room scale that is not too large, the K Nearest Neighbor (KNN) algorithm is used to calculate the error value generated from the reader and antenna that measures tags and is applied in an automatic counting program designed using the Python programming language. After testing and experimenting on target tags using each of the 4 reference tags, the test and experiment results do not differ greatly from the actual coordinates of the detected tags for target tag A with a percentage error coordinate value of (x = 6.3%), (y = 3%), and (z = 0.01%). The target Tag A error values of 4 reference tags are (x = 6.3 cm), (y = 1.05 cm), and (z = 0.4cm). Meanwhile, the coordinate error values (xe, ye, ze) for target B Tag with the percentage error coordinate values of (x = 3%), (y = 4%), and (z = 0%). The target Tag B error values of 4 reference tags are (x = 3.3 cm), (y = 2.8cm), and (z = 0 cm).
- Copyright
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ahmad Fali OKLILAS AU - Fajri Aulia RACHMAT AU - Purwita SARI PY - 2020 DA - 2020/05/06 TI - Implementation K-Nearest Neighbor Algorithm in Searching Location Books in Library Statically Based on RFID BT - Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019) PB - Atlantis Press SP - 300 EP - 305 SN - 1951-6851 UR - https://doi.org/10.2991/aisr.k.200424.045 DO - 10.2991/aisr.k.200424.045 ID - OKLILAS2020 ER -