Open AccessMETODE NILAI JARAK GUNA KESAMAAN ATAU KEMIRIPAN CIRI SUATU CITRA (KASUS DETEKSI AWAN CUMULONIMBUS MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS)
Author(s) -
Dwi Nugraheny
Publication year - 2017
Publication title -
angkasa
Language(s) - English
Resource type - Journals
eISSN - 2581-1355
pISSN - 2085-9503
DOI - 10.28989/angkasa.v7i2.145
Subject(s) - mahalanobis distance , euclidean distance , principal component analysis , mathematics , similarity (geometry) , statistics , block (permutation group theory) , pattern recognition (psychology) , artificial intelligence , image (mathematics) , combinatorics , computer science , geometry
One commonality or similarity matching phase characteristics of an image is by using the method of distance measurement. Distance is an important aspect in the development of methods of grouping and regression. Before the grouping of data or object to the detection process, first determined the size of the proximity distance between data elements. In this study, there will be a comparison of several methods including distance measurement using Euclidean distance, Manhattan/ City Block Distance, Mahalanobis which will be implemented in the case of cumulonimbus image clouds detection using Principal Component Analysis (PCA). The average percentage of accuracy of image similarity value Cumulonimbus clouds using the Euclidean distance method was 93 percent and the distance Manhattan/ City Block Distance is 90 percent, while the Mahalanobis distance method was 50 percent.