BibTeX
@article{IJASEIT6086,
author = {Asti Putri Rahmadini and Prima Kristalina and Amang Sudarsono},
title = {Optimization of Fingerprint Indoor Localization System for Multiple Object Tracking Based on Iterated Weighting Constant - KNN Method},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {8},
number = {3},
year = {2018},
pages = {998--1007},
keywords = {indoor localization; fingerprint; iterated weighting constant-KNN},
abstract = {Indoor localization promises a lot of benefits on the application in various fields. The fingerprint method is often used because it has high mobility, low network cost, and high compatibility. However, the distance and RSSI relationships are non-linear which decreases the accuracy of the system. KNN is required as a matching algorithm to solve the problem. The error result of Fingerprint-KNN system for indoor localization is still less satisfactory, therefore weighting factor is added in KNN algorithm as a modification to optimize the accuracy and precision of the localization system. The usual W-KNN is adding a value in form of the distance error from estimation result. In this paper, the constant as the result of iteration process within a range is multiplied by the error value which is added to the system as a weighting of KNN algorithm. The iterated weighting constant provides optimization on the system up to 25% better than the conventional system.},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6086},
doi = {10.18517/ijaseit.8.3.6086}
}
EndNote
%A Rahmadini, Asti Putri
%A Kristalina, Prima
%A Sudarsono, Amang
%D 2018
%T Optimization of Fingerprint Indoor Localization System for Multiple Object Tracking Based on Iterated Weighting Constant - KNN Method
%B 2018
%9 indoor localization; fingerprint; iterated weighting constant-KNN
%! Optimization of Fingerprint Indoor Localization System for Multiple Object Tracking Based on Iterated Weighting Constant - KNN Method
%K indoor localization; fingerprint; iterated weighting constant-KNN
%X Indoor localization promises a lot of benefits on the application in various fields. The fingerprint method is often used because it has high mobility, low network cost, and high compatibility. However, the distance and RSSI relationships are non-linear which decreases the accuracy of the system. KNN is required as a matching algorithm to solve the problem. The error result of Fingerprint-KNN system for indoor localization is still less satisfactory, therefore weighting factor is added in KNN algorithm as a modification to optimize the accuracy and precision of the localization system. The usual W-KNN is adding a value in form of the distance error from estimation result. In this paper, the constant as the result of iteration process within a range is multiplied by the error value which is added to the system as a weighting of KNN algorithm. The iterated weighting constant provides optimization on the system up to 25% better than the conventional system.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6086
%R doi:10.18517/ijaseit.8.3.6086
%J International Journal on Advanced Science, Engineering and Information Technology
%V 8
%N 3
%@ 2088-5334
IEEE
Asti Putri Rahmadini,Prima Kristalina and Amang Sudarsono,"Optimization of Fingerprint Indoor Localization System for Multiple Object Tracking Based on Iterated Weighting Constant - KNN Method," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 3, pp. 998-1007, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.3.6086.
RefMan/ProCite (RIS)
TY - JOUR
AU - Rahmadini, Asti Putri
AU - Kristalina, Prima
AU - Sudarsono, Amang
PY - 2018
TI - Optimization of Fingerprint Indoor Localization System for Multiple Object Tracking Based on Iterated Weighting Constant - KNN Method
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 3
Y2 - 2018
SP - 998
EP - 1007
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - indoor localization; fingerprint; iterated weighting constant-KNN
N2 - Indoor localization promises a lot of benefits on the application in various fields. The fingerprint method is often used because it has high mobility, low network cost, and high compatibility. However, the distance and RSSI relationships are non-linear which decreases the accuracy of the system. KNN is required as a matching algorithm to solve the problem. The error result of Fingerprint-KNN system for indoor localization is still less satisfactory, therefore weighting factor is added in KNN algorithm as a modification to optimize the accuracy and precision of the localization system. The usual W-KNN is adding a value in form of the distance error from estimation result. In this paper, the constant as the result of iteration process within a range is multiplied by the error value which is added to the system as a weighting of KNN algorithm. The iterated weighting constant provides optimization on the system up to 25% better than the conventional system.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6086
DO - 10.18517/ijaseit.8.3.6086
RefWorks
RT Journal Article
ID 6086
A1 Rahmadini, Asti Putri
A1 Kristalina, Prima
A1 Sudarsono, Amang
T1 Optimization of Fingerprint Indoor Localization System for Multiple Object Tracking Based on Iterated Weighting Constant - KNN Method
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 3
YR 2018
SP 998
OP 1007
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 indoor localization; fingerprint; iterated weighting constant-KNN
AB Indoor localization promises a lot of benefits on the application in various fields. The fingerprint method is often used because it has high mobility, low network cost, and high compatibility. However, the distance and RSSI relationships are non-linear which decreases the accuracy of the system. KNN is required as a matching algorithm to solve the problem. The error result of Fingerprint-KNN system for indoor localization is still less satisfactory, therefore weighting factor is added in KNN algorithm as a modification to optimize the accuracy and precision of the localization system. The usual W-KNN is adding a value in form of the distance error from estimation result. In this paper, the constant as the result of iteration process within a range is multiplied by the error value which is added to the system as a weighting of KNN algorithm. The iterated weighting constant provides optimization on the system up to 25% better than the conventional system.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6086
DO - 10.18517/ijaseit.8.3.6086
